--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Political_predispositions\log_political_predisposition.log
  log type:  text
 opened on:  16 Aug 2018, 10:03:11

. 
. ********************************************************************
. ***Analyses for "Political Predispositions, not Popularity...*******
. ********************************************************************
. ***This do-file is made for Stata 15********************************
. ********************************************************************
. 
. ****************************************************************************************************************************************************
. ***INSTALLING PACKAGES******************************************************************************************************************************
. ****************************************************************************************************************************************************
.         
. *requires the package "grc1leg" in order to produce the combined figures
. net install grc1leg2.pkg, from("http://digital.cgdev.org/doc/stata/MO/Misc")
checking grc1leg2 consistency and verifying not already installed...
all files already exist and are up to date.

. 
. *requires the package "blindschemes" in order to produce the figures
. *ssc install blindschemes
. set scheme plotplain

. 
. *Setting the font in the figures:
. graph set window fontface "Gill Sans MT"

. 
. ***************************************************************************************************************************************************
. ***Getting the data********************************************************************************************************************************
. ***************************************************************************************************************************************************
. clear all

. set more off

. 
. use "C:\Political_predispositions\170411 Final dataset SoMePol DEL1.dta"
( )

. 
. 
. **********************************************************************************************************************************************************
. ***Recoding, Creating variables and Attaching Value Labels************************************************************************************************
. **********************************************************************************************************************************************************
. 
. *Vote
. recode fvalg_mo (1=1) (else=0), gen(vote_S)
(2328 differences between fvalg_mo and vote_S)

. recode fvalg_mo (8=1) (else=0), gen(vote_V)
(3012 differences between fvalg_mo and vote_V)

. 
. *Previous vote
. recode fvalg15 (1=1) (else=0), gen(vote_pr_S)
(2249 differences between fvalg15 and vote_pr_S)

. recode fvalg15 (8=1) (else=0), gen(vote_pr_V)
(3012 differences between fvalg15 and vote_pr_V)

. 
. **LEFT_RIGHT - placement of self and parties**
. recode s_47 s_12 s_13 s_14 s_15 s_16 s_17 s_18 s_19 s_20 s_21 (12=.)
(s_47: 340 changes made)
(s_12: 327 changes made)
(s_13: 460 changes made)
(s_14: 427 changes made)
(s_15: 831 changes made)
(s_16: 451 changes made)
(s_17: 459 changes made)
(s_18: 367 changes made)
(s_19: 334 changes made)
(s_20: 431 changes made)
(s_21: 732 changes made)

. gen LR_respondent=s_47-1
(340 missing values generated)

. gen LR_A=s_12-1 
(327 missing values generated)

. gen LR_B=s_13-1
(460 missing values generated)

. gen LR_C=s_14-1 
(427 missing values generated)

. gen LR_D=s_15-1 
(831 missing values generated)

. gen LR_F=s_16-1 
(451 missing values generated)

. gen LR_I=s_17-1 
(459 missing values generated)

. gen LR_O=s_18-1 
(367 missing values generated)

. gen LR_V=s_19-1 
(334 missing values generated)

. gen LR_Ø=s_20-1 
(431 missing values generated)

. gen LR_Å=s_21-1 
(732 missing values generated)

. 
. by fvalg_mo, sort: egen fvalg_mo_median = median(LR_respondent) //respondents not placing themselves on LR are assigned the median value among respondents with same vote choice

. replace LR_respondent=fvalg_mo_median if LR_respondent==.
(340 real changes made)

. 
. **Immigration is a threat**
. recode s_1 (99=.), gen(immigration_is_threat)
(40 differences between s_1 and immigration_is_threat)

. 
. **Interest in politics**
. recode s_22 (12=.), gen(uns_interest)
(13 differences between s_22 and uns_interest)

. gen interest=(uns_interest-1)/10
(13 missing values generated)

. 
. **Party sympathy**
. recode s_24 s_25 s_26 s_27 s_28 s_29 s_30 s_31 s_32 s_33 (12=.)
(s_24: 209 changes made)
(s_25: 337 changes made)
(s_26: 318 changes made)
(s_27: 759 changes made)
(s_28: 345 changes made)
(s_29: 286 changes made)
(s_30: 210 changes made)
(s_31: 210 changes made)
(s_32: 304 changes made)
(s_33: 500 changes made)

. gen PS_A=s_24-1
(209 missing values generated)

. gen PS_B=s_25-1
(337 missing values generated)

. gen PS_C=s_26-1
(318 missing values generated)

. gen PS_D=s_27-1
(759 missing values generated)

. gen PS_F=s_28-1
(345 missing values generated)

. gen PS_I=s_29-1
(286 missing values generated)

. gen PS_O=s_30-1
(210 missing values generated)

. gen PS_V=s_31-1
(210 missing values generated)

. gen PS_ENH=s_32-1
(304 missing values generated)

. gen PS_AA=s_33-1
(500 missing values generated)

.         
.         sum PS_A

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        PS_A |      2,803    5.410988    2.463621          0         10

.         gen PS_A_minus_sd=(r(mean)-r(sd))

.         gen PS_A_plus_sd=(r(mean)+r(sd))

. 
.         sum PS_V

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        PS_V |      2,802    3.845111    2.821146          0         10

.         gen PS_V_minus_sd=(r(mean)-r(sd))

.         gen PS_V_plus_sd=(r(mean)+r(sd))

. 
.         
. **Party leader sympathy**
. recode s_202 s_209 s_210 s_211 s_212 s_213 s_214 s_215 s_216 s_217 (12=.)
(s_202: 363 changes made)
(s_209: 554 changes made)
(s_210: 419 changes made)
(s_211: 862 changes made)
(s_212: 513 changes made)
(s_213: 337 changes made)
(s_214: 309 changes made)
(s_215: 224 changes made)
(s_216: 504 changes made)
(s_217: 399 changes made)

. gen PLS_MetteF=s_202-1 
(363 missing values generated)

. gen PLS_MortenO=s_209-1
(554 missing values generated)

. gen PLS_SorenP= s_210-1
(419 missing values generated)

. gen PLS_PernilleV=s_211-1
(862 missing values generated)

. gen PLS_PiaOD=s_212-1
(513 missing values generated)

. gen PLS_AndersS=s_213-1
(337 missing values generated)

. gen PLS_KristianT=s_214-1
(309 missing values generated)

. gen PLS_LarsL=s_215-1
(224 missing values generated)

. gen PLS_PernilleS=s_216-1
(504 missing values generated)

. gen PLS_UffeE=s_217-1
(399 missing values generated)

. 
. **Talking politics**
. recode s_23 (9=.), gen(uns_talkingpolitics)
(94 differences between s_23 and uns_talkingpolitics)

. gen talkingpolitics=(8-uns_talkingpolitics)/7 //
(94 missing values generated)

. 
. **External Efficacy**
. recode s_4 s_36 s_35 s_9 s_7 (99=.)
(s_4: 142 changes made)
(s_36: 104 changes made)
(s_35: 140 changes made)
(s_9: 190 changes made)
(s_7: 127 changes made)

. alpha s_4 s_36 s_35 s_9 s_7, item // s_35 has negative impact on scale reliability and is therefore removed

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_4          | 2870    +       0.6746        0.4402        .3414864      0.6011
s_36         | 2908    -       0.5846        0.3819        .3926523      0.6385
s_35         | 2872    +       0.4969        0.2331        .4639692      0.6936
s_9          | 2822    +       0.7100        0.4890        .3181029      0.5827
s_7          | 2885    +       0.7306        0.4868        .2939502      0.5719
-------------+-----------------------------------------------------------------
Test scale   |                                             .3619594      0.6725
-------------------------------------------------------------------------------

. alpha s_4 s_36 s_9 s_7 , item reverse(s_4 s_9 s_7) gen(uns_external_efficacy)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_4          | 2870    -       0.7078        0.4473        .4599788      0.6207
s_36         | 2908    +       0.6112        0.3875        .5573937      0.6787
s_9          | 2822    -       0.7361        0.4919        .4329573      0.6058
s_7          | 2885    -       0.7462        0.4721        .4063459      0.6042
-------------+-----------------------------------------------------------------
Test scale   |                                             .4639692      0.6936
-------------------------------------------------------------------------------

. gen external_efficacy=(uns_external_efficacy+5)/10
(75 missing values generated)

. 
. **Internal Efficacy**
. recode s_34 s_10 s_8 s_6 s_5 (99=.)
(s_34: 121 changes made)
(s_10: 153 changes made)
(s_8: 121 changes made)
(s_6: 179 changes made)
(s_5: 200 changes made)

. alpha s_34 s_10 s_8 s_6 s_5, item gen(uns_internal_efficacy)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_34         | 2891    -       0.7098        0.5477        .4163675      0.7052
s_10         | 2859    +       0.6668        0.4499        .4748325      0.7257
s_8          | 2891    -       0.6932        0.5248        .4459821      0.7118
s_6          | 2833    +       0.7182        0.5425        .4638493      0.7107
s_5          | 2812    +       0.6026        0.4195        .5479106      0.7459
-------------+-----------------------------------------------------------------
Test scale   |                                             .4699477      0.7636
-------------------------------------------------------------------------------

. gen internal_efficacy=(uns_internal_efficacy+5)/10
(82 missing values generated)

. 
. **Need for Closure**
. recode s_37 s_52 s_51 s_50 s_49 s_48 s_46 s_45 s_44 s_43 s_42 s_41 s_40 s_39 s_38 (99=.)
(s_37: 167 changes made)
(s_52: 185 changes made)
(s_51: 142 changes made)
(s_50: 345 changes made)
(s_49: 189 changes made)
(s_48: 203 changes made)
(s_46: 232 changes made)
(s_45: 158 changes made)
(s_44: 144 changes made)
(s_43: 317 changes made)
(s_42: 273 changes made)
(s_41: 219 changes made)
(s_40: 154 changes made)
(s_39: 223 changes made)
(s_38: 197 changes made)

. alpha s_37 s_52 s_51 s_50 s_49 s_48 s_46 s_45 s_44 s_43 s_42 s_41 s_40 s_39 s_38 , item gen(uns_NFCLOSURE)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_37         | 2845    +       0.5275        0.4397        .5534606      0.8844
s_52         | 2827    +       0.6393        0.5504        .5239813      0.8804
s_51         | 2870    +       0.7018        0.6380        .5250483      0.8760
s_50         | 2667    +       0.6422        0.5686        .5317206      0.8789
s_49         | 2823    +       0.5390        0.4483        .5511956      0.8841
s_48         | 2809    +       0.7171        0.6538        .5202505      0.8753
s_46         | 2780    +       0.4957        0.4229        .5669929      0.8846
s_45         | 2854    +       0.5130        0.4351        .5617196      0.8845
s_44         | 2868    +       0.6062        0.5278        .5409241      0.8810
s_43         | 2695    +       0.6209        0.5408        .5341663      0.8802
s_42         | 2739    +       0.6340        0.5575        .5330176      0.8796
s_41         | 2793    +       0.7366        0.6800        .5192538      0.8743
s_40         | 2858    +       0.6980        0.6362        .5275685      0.8763
s_39         | 2789    +       0.4876        0.3834        .5555454      0.8874
s_38         | 2815    +       0.7628        0.7102        .5143321      0.8728
-------------+-----------------------------------------------------------------
Test scale   |                                             .5372767      0.8872
-------------------------------------------------------------------------------

. gen NFCLOSURE=(uns_NFCLOSURE-1)/5
(68 missing values generated)

. egen float NFCLOSURE_split = cut(NFCLOSURE), group(2) icodes //median split for quick calculations
(68 missing values generated)

. 
. **Need for Cognition**
. recode s_53 s_54 s_59 s_58 s_56 s_55 (99=.)
(s_53: 187 changes made)
(s_54: 118 changes made)
(s_59: 116 changes made)
(s_58: 124 changes made)
(s_56: 137 changes made)
(s_55: 123 changes made)

. alpha s_53 s_54 s_59 s_58 s_56 s_55, item reverse(s_59 s_58 s_56) gen(uns_NFCOGNITION)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_53         | 2825    +       0.5433        0.3481        .2809522      0.7011
s_54         | 2894    +       0.5872        0.4060         .265928      0.6844
s_59         | 2896    -       0.6315        0.4125        .2430057      0.6733
s_58         | 2888    -       0.7162        0.5428        .2118483      0.6320
s_56         | 2875    -       0.6824        0.4964        .2269528      0.6529
s_55         | 2889    +       0.5005        0.2703        .2928541      0.7244
-------------+-----------------------------------------------------------------
Test scale   |                                             .2536223      0.7184
-------------------------------------------------------------------------------

. gen NFCOGNITION=((uns_NFCOGNITION+3)/8)
(71 missing values generated)

. 
. **Need to Evaluate
. recode s_68 s_67 s_66 s_65 s_64 s_63 s_62 s_61 s_60 (99=.)
(s_68: 75 changes made)
(s_67: 90 changes made)
(s_66: 104 changes made)
(s_65: 110 changes made)
(s_64: 97 changes made)
(s_63: 158 changes made)
(s_62: 115 changes made)
(s_61: 395 changes made)
(s_60: 122 changes made)

. alpha s_68 s_67 s_66 s_65 s_64 s_63 s_62 s_61 s_60, item gen(uns_NTEVALUATE)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
s_68         | 2937    +       0.6698        0.5565        .2634999      0.7660
s_67         | 2922    +       0.6113        0.4894        .2755299      0.7767
s_66         | 2908    -       0.5243        0.3713        .2886162      0.7940
s_65         | 2902    -       0.5339        0.3852        .2840816      0.7914
s_64         | 2915    -       0.6445        0.5181        .2607904      0.7731
s_63         | 2854    +       0.4239        0.2533         .306948      0.8109
s_62         | 2897    +       0.7086        0.5846        .2486099      0.7610
s_61         | 2617    +       0.7089        0.5926        .2508502      0.7632
s_60         | 2890    +       0.6060        0.4745        .2738796      0.7802
-------------+-----------------------------------------------------------------
Test scale   |                                             .2724917      0.7998
-------------------------------------------------------------------------------

. gen NTEVALUATE=(uns_NTEVALUATE+3)/8
(57 missing values generated)

. 
. recode s_70 (1=0)(2=1) (3=2) (4=3) (5=4) (6=5) (7=6) (8=7) (99=.), gen("Newspaper_use")
(3012 differences between s_70 and Newspaper_use)

. recode s_71 (1=0)(2=1) (3=2) (4=3) (5=4) (6=5) (7=6) (8=7) (99=.), gen("TV_News_use")
(3012 differences between s_71 and TV_News_use)

. 
. **Minutes on facebook
. gen Minutes_on_Facebook=minut2
(1,049 missing values generated)

. recode Minutes_on_Facebook (120/4000=120), gen(Minutes_on_facebook_winsorized)
(74 differences between Minutes_on_Facebook and Minutes_on_facebook_winsorized)

. 
. **Disagreement with friends on Facebook (never-always)
. recode s_98 (99=.), gen(Facebook_disagree) 
(612 differences between s_98 and Facebook_disagree)

. 
. **Self censorship on Facebook
. recode s_111 (99=.), gen(Facebook_selfcensor)
(206 differences between s_111 and Facebook_selfcensor)

. 
. **Female
. recode koentot (1=0) (2=1), gen(female)
(3012 differences between koentot and female)

. 
. **Education
. recode udd_new (1/5 = 0) (6/8 = 1), gen(somecollege)
(3012 differences between udd_new and somecollege)

. 
. *********************************************************************************************
. ***EXPERIMENT********************************************************************************
. *********************************************************************************************
. 
. 
. *******************************
. ***EXPERIMENTAL TREATMENTS*****
. 
. 
. ***SPONSORSHIP****************************************************
. 
. ***IMMIGRANTS ***
. gen immigrants_MetteF=.
(3,012 missing values generated)

. recode immigrants_MetteF (.=1) if mf31_1!=. | mf32_1!=. | mf33_1!=. | mf34_1!=. | mf35_1!=. | mf36_1!=. 
(immigrants_MetteF: 1494 changes made)

. recode immigrants_MetteF (.=0) if ll41_!=. | ll42_1!=. | ll43_1!=. | ll44_1!=. | ll45_1!=. | ll46_1!=. 
(immigrants_MetteF: 1518 changes made)

. 
. 
. 
. ***VETERANS ***
. gen vet_MetteF=.
(3,012 missing values generated)

. recode vet_MetteF (.=1) if mf11_1!=. | mf12_1!=. | mf13_1!=. | mf14_1!=. | mf15_1!=. | mf16_1!=. | mf11a_1!=. | mf12a_1!=. | mf13a_1!=. | mf14a_1!=. | mf15a_1!=. | mf16a_1!=.  
(vet_MetteF: 1521 changes made)

. recode vet_MetteF (.=0) if ll21_1!=. | ll22_1!=. | ll23_1!=. | ll24_1!=. | ll25_1!=. | ll26_1!=. | ll21a_1!=. | ll22a_1!=. | ll23a_1!=. | ll24a_1!=. | ll25a_1!=. | ll26a_1!=.  
(vet_MetteF: 1491 changes made)

. 
. 
. ***NUMBER OF LIKES*************************************************
. 
. ***Immigrants***
. gen many_likes_immi=. 
(3,012 missing values generated)

. recode many_likes_immi (.=1) if mf31_1!=. |  mf33_1!=. |  mf35_1!=. |  ll41_!=. |  ll43_1!=. |  ll45_1!=.  
(many_likes_immi: 1499 changes made)

. recode many_likes_immi (.=0) if mf32_1!=. |  mf34_1!=. |  mf36_1!=. |  ll42_1!=. |  ll44_1!=. |  ll46_1!=.  
(many_likes_immi: 1513 changes made)

. 
. 
. ***Veterans***
. gen many_likes_vet=. 
(3,012 missing values generated)

. recode many_likes_vet (.=1) if mf11_1!=. |  mf13_1!=. |  mf15_1!=. |  ll21_1!=. |  ll23_1!=. |  ll25_1!=. | mf11a_1!=. |  mf13a_1!=. |  mf15a_1!=. |  ll21a_1!=. |  ll23a_1!=. |  ll25a_1!=.  
(many_likes_vet: 1532 changes made)

. recode many_likes_vet (.=0) if mf12_1!=. |  mf14_1!=. |  mf16_1!=. |  ll22_1!=. |  ll24_1!=. |  ll26_1!=. | mf12a_1!=. |  mf14a_1!=. |  mf16a_1!=. |  ll22a_1!=. |  ll24a_1!=. |  ll26a_1!=.  
(many_likes_vet: 1480 changes made)

. 
. 
. ***COMMENTS**********************************************************
.  
.  ***Immigrants***
. gen comments_immi=.
(3,012 missing values generated)

. recode comments_immi (.=0) if mf31_1!=. |  mf32_1!=. |  ll41_!=.  |  ll42_1!=.  //NOTE: the variable "mf41_" is named without a finishing "1" in the original dataset) 
(comments_immi: 1022 changes made)

. recode comments_immi (.=1) if mf33_1!=. |  mf34_1!=. |  ll43_1!=. |  ll44_1!=. 
(comments_immi: 1015 changes made)

. recode comments_immi (.=2) if mf35_1!=. |  mf36_1!=. |  ll45_1!=. |  ll46_1!=. 
(comments_immi: 975 changes made)

. 
. 
. ***Veterans***
. gen comments_vet=.
(3,012 missing values generated)

. recode comments_vet (.=0) if mf11_1!=. |  mf12_1!=. |  ll21_1!=. |  ll22_1!=. |  mf11a_1!=. |  mf12a_1!=. |  ll21a_1!=. |  ll22a_1!=.  
(comments_vet: 1025 changes made)

. recode comments_vet (.=1) if mf13_1!=. |  mf14_1!=. |  ll23_1!=. |  ll24_1!=. |  mf13a_1!=. |  mf14a_1!=. |  ll23a_1!=. |  ll24a_1!=.  
(comments_vet: 993 changes made)

. recode comments_vet (.=2) if mf15_1!=. |  mf16_1!=. |  ll25_1!=. |  ll26_1!=. |  mf15a_1!=. |  mf16a_1!=. |  ll25a_1!=. |  ll26a_1!=. 
(comments_vet: 994 changes made)

. 
. 
. label define comments_label 0 "No comments" 1 "Positive Comments" 2 "Negative Comments"

. label values comments_immi comments_vet

. 
. 
. ********************************************************************
. ***EXPERIMENTAL DV'S************************************************
. 
. 
. ***FACEBOOK BEHAVIOR************************************************
. 
. **liking** 
. gen like_vet_MF=max(mf11_1, mf12_1, mf13_1, mf14_1, mf15_1, mf16_1, mf11a_1, mf12a_1, mf13a_1, mf14a_1, mf15a_1, mf16a_1)
(1,491 missing values generated)

. gen like_vet_LL=max(ll21_1, ll22_1, ll23_1, ll24_1, ll25_1, ll26_1, ll21a_1, ll22a_1, ll23a_1, ll24a_1, ll25a_1, ll26a_1)
(1,521 missing values generated)

. gen like_vet_both=max(like_vet_MF, like_vet_LL)

. 
. gen like_immi_MF=max(mf31_1, mf32_1, mf33_1, mf34_1, mf35_1, mf36_1)
(1,518 missing values generated)

. gen like_immi_LL=max(ll41_, ll42_1, ll43_1, ll44_1, ll45_1, ll46_1)   //NOTE: the variable "mf41_" is named without a finishing "1" in the original dataset)    
(1,494 missing values generated)

. gen like_immi_both=max(like_immi_MF , like_immi_LL)

. 
. label define reaction 1 "Yes" 2 "Maybe" 3 "No"

. label values like_vet_MF like_vet_LL like_vet_both like_immi_MF like_immi_LL like_immi_both reaction

. 
. recode like_immi_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(like_immi_both_yes)
(2650 differences between like_immi_both and like_immi_both_yes)

. recode like_immi_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(like_immi_both_no)
(3012 differences between like_immi_both and like_immi_both_no)

. 
. recode like_vet_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(like_vet_both_yes)
(2413 differences between like_vet_both and like_vet_both_yes)

. recode like_vet_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(like_vet_both_no)
(3012 differences between like_vet_both and like_vet_both_no)

. 
. 
. **Comment** 
. gen comment_vet_MF=max(mf11_2, mf12_2, mf13_2, mf14_2, mf15_2, mf16_2, mf11a_2, mf12a_2, mf13a_2, mf14a_2, mf15a_2, mf16a_2)
(1,491 missing values generated)

. gen comment_vet_LL=max(ll21_2, ll22_2, ll23_2, ll24_2, ll25_2, ll26_2, ll21a_2, ll22a_2, ll23a_2, ll24a_2, ll25a_2, ll26a_2)
(1,521 missing values generated)

. gen comment_vet_both=max(comment_vet_MF, comment_vet_LL)

. 
. gen comment_immi_MF=max(mf31_2, mf32_2, mf33_2, mf34_2, mf35_2, mf36_2)
(1,518 missing values generated)

. gen comment_immi_LL=max(ll41_2, ll42_2, ll43_2, ll44_2, ll45_2, ll46_2)   
(1,494 missing values generated)

. gen comment_immi_both=max(comment_immi_MF , comment_immi_LL)

. 
. 
. label values comment_vet_MF comment_vet_LL comment_vet_both comment_immi_MF comment_immi_LL comment_immi_both reaction

. 
. recode comment_immi_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(comment_immi_both_yes)
(2915 differences between comment_immi_both and comment_immi_both_yes)

. recode comment_immi_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(comment_immi_both_no)
(3012 differences between comment_immi_both and comment_immi_both_no)

. 
. recode comment_vet_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(comment_vet_both_yes)
(2936 differences between comment_vet_both and comment_vet_both_yes)

. recode comment_vet_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(comment_vet_both_no)
(3012 differences between comment_vet_both and comment_vet_both_no)

. 
. 
. **Share** 
. gen share_vet_MF=max(mf11_3, mf12_3, mf13_3, mf14_3, mf15_3, mf16_3, mf11a_3, mf12a_3, mf13a_3, mf14a_3, mf15a_3, mf16a_3)
(1,491 missing values generated)

. gen share_vet_LL=max(ll21_3, ll22_3, ll23_3, ll24_3, ll25_3, ll26_3, ll21a_3, ll22a_3, ll23a_3, ll24a_3, ll25a_3, ll26a_3)
(1,521 missing values generated)

. gen share_vet_both=max(share_vet_MF, share_vet_LL)

. 
. gen share_immi_MF=max(mf31_3, mf32_3, mf33_3, mf34_3, mf35_3, mf36_3)
(1,518 missing values generated)

. gen share_immi_LL=max(ll41_3, ll42_3, ll43_3, ll44_3, ll45_3, ll46_3)   
(1,494 missing values generated)

. gen share_immi_both=max(share_immi_MF , share_immi_LL)

. 
. 
. label values share_vet_MF share_vet_LL share_vet_both share_immi_MF share_immi_LL share_immi_both reaction

.         
. recode share_immi_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(share_immi_both_yes)
(2930 differences between share_immi_both and share_immi_both_yes)

. recode share_immi_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(share_immi_both_no)
(3012 differences between share_immi_both and share_immi_both_no)

. 
. recode share_vet_both (1=1 "Yes") (2 3=0 "Maybe/No"), gen(share_vet_both_yes)
(2893 differences between share_vet_both and share_vet_both_yes)

. recode share_vet_both (3=1 "No") (1 2=0 "Maybe/yes") , gen(share_vet_both_no)
(3012 differences between share_vet_both and share_vet_both_no)

. 
. 
. 
. ***POLICY ATTITUDES************************************************
. 
. **Veterans
. recode vet1kr (8=.)
(vet1kr: 742 changes made)

. gen vet1kr_std=((vet1kr-1)*4/6)+1
(742 missing values generated)

. recode vet1uds1 vet1uds2 vet1uds3 vet1uds4 vet1uds5 (99=.)
(vet1uds1: 477 changes made)
(vet1uds2: 205 changes made)
(vet1uds3: 313 changes made)
(vet1uds4: 323 changes made)
(vet1uds5: 222 changes made)

. recode vet1uds2 (5=1) (4=2) (3=3) (2=4) (1=5), gen(vet1uds2_rev)
(2257 differences between vet1uds2 and vet1uds2_rev)

. alpha vet1kr_std vet1uds1 vet1uds2_rev vet1uds3 vet1uds4 vet1uds5, item gen(uns_veterans)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
vet1kr_std   | 2270    +       0.7854        0.6868        .4642995      0.7972
vet1uds1     | 2535    +       0.8405        0.7531        .4366011      0.7824
vet1uds2_rev | 2807    +       0.6867        0.4810        .4878832      0.8270
vet1uds3     | 2699    +       0.8223        0.7163        .4376253      0.7865
vet1uds4     | 2689    +       0.7767        0.6324        .4450652      0.8049
vet1uds5     | 2790    +       0.5735        0.3532        .5575073      0.8629
-------------+-----------------------------------------------------------------
Test scale   |                                             .4711914      0.8376
-------------------------------------------------------------------------------

. gen veterans=(uns_veterans-1)/4
(143 missing values generated)

. 
. **immigrants
. recode indkr (8=.)
(indkr: 477 changes made)

. gen indkr_std=((indkr-1)*4/6)+1
(477 missing values generated)

. recode induds1 induds2 induds3 induds4 induds5 (99=.)
(induds1: 246 changes made)
(induds2: 199 changes made)
(induds3: 159 changes made)
(induds4: 173 changes made)
(induds5: 119 changes made)

. recode induds1 (5=1) (4=2) (3=3) (2=4) (1=5), gen(induds1_rev)
(2096 differences between induds1 and induds1_rev)

. recode induds3 (5=1) (4=2) (3=3) (2=4) (1=5), gen(induds3_rev)
(2160 differences between induds3 and induds3_rev)

. recode induds4 (5=1) (4=2) (3=3) (2=4) (1=5), gen(induds4_rev)
(2189 differences between induds4 and induds4_rev)

. recode induds5 (5=1) (4=2) (3=3) (2=4) (1=5), gen(induds5_rev)
(2291 differences between induds5 and induds5_rev)

. alpha indkr_std induds1_rev induds2 induds3_rev induds4_rev induds5_rev, item gen(uns_immigrants)

Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
indkr_std    | 2535    +       0.8217        0.7326        .8045828      0.8311
induds1_rev  | 2766    +       0.7768        0.6576         .825876      0.8444
induds2      | 2813    +       0.6249        0.4510        .9450931      0.8804
induds3_rev  | 2853    +       0.7797        0.6643        .8366239      0.8438
induds4_rev  | 2839    +       0.8411        0.7429        .7625983      0.8280
induds5_rev  | 2893    +       0.8002        0.6910        .8142804      0.8374
-------------+-----------------------------------------------------------------
Test scale   |                                              .831361      0.8673
-------------------------------------------------------------------------------

. gen immigrants=(uns_immigrants-1)/4 
(70 missing values generated)

. 
. 
. 
. **********************************************************************************************************************************************************
. ***Analyses***********************************************************************************************************************************************
. **********************************************************************************************************************************************************
. 
. ***Descriptives***
. tab female

  RECODE of |
    koentot |
      (Køn) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,453       48.24       48.24
          1 |      1,559       51.76      100.00
------------+-----------------------------------
      Total |      3,012      100.00

. sum age

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         age |      3,012    47.71979     15.8548         18         75

. tab somecollege

  RECODE of |
    udd_new |
   (Hvad er |
        den |
    højeste |
 uddannelse |
     du har |
 gennemført |
         ?) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,305       43.33       43.33
          1 |      1,707       56.67      100.00
------------+-----------------------------------
      Total |      3,012      100.00

. sum LR_respondent

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
LR_respond~t |      3,012    4.729748    2.468299          0         10

. sum PS_A 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        PS_A |      2,803    5.410988    2.463621          0         10

. sum PS_V

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        PS_V |      2,802    3.845111    2.821146          0         10

. sum veterans

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    veterans |      2,869     .580071    .1919125          0          1

. sum immigrants

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  immigrants |      2,942    .4765947    .2445401          0          1

. 
. *Party sympathy is more important than party leader sympathy for vote choice:
.         corr vote_S PLS_MetteF 
(obs=2,649)

             |   vote_S PLS_Me~F
-------------+------------------
      vote_S |   1.0000
  PLS_MetteF |   0.4529   1.0000


.         corr vote_S PS_A
(obs=2,803)

             |   vote_S     PS_A
-------------+------------------
      vote_S |   1.0000
        PS_A |   0.5236   1.0000


.         pwcorr vote_S PLS_MetteF PS_A

             |   vote_S PLS_Me~F     PS_A
-------------+---------------------------
      vote_S |   1.0000 
  PLS_MetteF |   0.4529   1.0000 
        PS_A |   0.5236   0.7505   1.0000 

. 
.         corr vote_V PLS_LarsL
(obs=2,788)

             |   vote_V PLS_La~L
-------------+------------------
      vote_V |   1.0000
   PLS_LarsL |   0.4433   1.0000


.         corr vote_V PS_V
(obs=2,802)

             |   vote_V     PS_V
-------------+------------------
      vote_V |   1.0000
        PS_V |   0.5056   1.0000


.         pwcorr vote_V PLS_LarsL PS_V

             |   vote_V PLS_La~L     PS_V
-------------+---------------------------
      vote_V |   1.0000 
   PLS_LarsL |   0.4433   1.0000 
        PS_V |   0.5056   0.8355   1.0000 

.         
.         *(the same is true for previous vote)
.         corr vote_pr_S PLS_MetteF 
(obs=2,649)

             | vote_p~S PLS_Me~F
-------------+------------------
   vote_pr_S |   1.0000
  PLS_MetteF |   0.3879   1.0000


.         corr vote_pr_S PS_A
(obs=2,803)

             | vote_p~S     PS_A
-------------+------------------
   vote_pr_S |   1.0000
        PS_A |   0.4767   1.0000


.         
.         corr vote_pr_V PLS_LarsL
(obs=2,788)

             | vote_p~V PLS_La~L
-------------+------------------
   vote_pr_V |   1.0000
   PLS_LarsL |   0.4267   1.0000


.         corr vote_pr_V PS_V
(obs=2,802)

             | vote_p~V     PS_V
-------------+------------------
   vote_pr_V |   1.0000
        PS_V |   0.4868   1.0000


.         
. *Balance
. logit female i.immigrants_MetteF i.many_likes_immi i.comments_immi //model insignificant

Iteration 0:   log likelihood = -2085.8937  
Iteration 1:   log likelihood = -2084.8879  
Iteration 2:   log likelihood = -2084.8878  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(4)        =       2.01
                                                Prob > chi2       =     0.7336
Log likelihood = -2084.8878                     Pseudo R2         =     0.0005

-------------------------------------------------------------------------------------
             female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
1.immigrants_MetteF |   .0561393   .0729717     0.77   0.442    -.0868826    .1991612
  1.many_likes_immi |  -.0434942   .0729848    -0.60   0.551    -.1865417    .0995534
                    |
      comments_immi |
                 1  |   .0664177   .0887171     0.75   0.454    -.1074646    .2402999
                 2  |   .0884538   .0896357     0.99   0.324    -.0872289    .2641365
                    |
              _cons |   .0132443   .0800325     0.17   0.869    -.1436164    .1701051
-------------------------------------------------------------------------------------

. logit female i.vet_MetteF i.many_likes_vet i.comments_vet //model insignificant

Iteration 0:   log likelihood = -2085.8937  
Iteration 1:   log likelihood = -2083.3074  
Iteration 2:   log likelihood = -2083.3074  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(4)        =       5.17
                                                Prob > chi2       =     0.2700
Log likelihood = -2083.3074                     Pseudo R2         =     0.0012

----------------------------------------------------------------------------------
          female |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    1.vet_MetteF |  -.0286723   .0730152    -0.39   0.695    -.1717795     .114435
1.many_likes_vet |  -.1149362   .0730298    -1.57   0.116    -.2580721    .0281996
                 |
    comments_vet |
              1  |  -.0144262   .0892467    -0.16   0.872    -.1893466    .1604942
              2  |  -.1276886   .0891773    -1.43   0.152     -.302473    .0470957
                 |
           _cons |   .1903593   .0810803     2.35   0.019     .0314448    .3492738
----------------------------------------------------------------------------------

. 
. logit somecollege i.immigrants_MetteF i.many_likes_immi i.comments_immi //model insignificant

Iteration 0:   log likelihood = -2060.8524  
Iteration 1:   log likelihood =  -2058.978  
Iteration 2:   log likelihood =  -2058.978  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(4)        =       3.75
                                                Prob > chi2       =     0.4411
Log likelihood =  -2058.978                     Pseudo R2         =     0.0009

-------------------------------------------------------------------------------------
        somecollege |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
1.immigrants_MetteF |   -.090197   .0736035    -1.23   0.220    -.2344572    .0540632
  1.many_likes_immi |   .0548676   .0736195     0.75   0.456     -.089424    .1991591
                    |
      comments_immi |
                 1  |   .0988746   .0896278     1.10   0.270    -.0767927    .2745418
                 2  |  -.0016103   .0901982    -0.02   0.986    -.1783956     .175175
                    |
              _cons |    .253515   .0806774     3.14   0.002     .0953902    .4116397
-------------------------------------------------------------------------------------

. logit somecollege i.vet_MetteF i.many_likes_vet i.comments_vet //model insignificant

Iteration 0:   log likelihood = -2060.8524  
Iteration 1:   log likelihood = -2056.8677  
Iteration 2:   log likelihood = -2056.8674  
Iteration 3:   log likelihood = -2056.8674  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(4)        =       7.97
                                                Prob > chi2       =     0.0927
Log likelihood = -2056.8674                     Pseudo R2         =     0.0019

----------------------------------------------------------------------------------
     somecollege |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    1.vet_MetteF |     .08386   .0736572     1.14   0.255    -.0605055    .2282256
1.many_likes_vet |  -.1783838   .0736953    -2.42   0.015    -.3228239   -.0339437
                 |
    comments_vet |
              1  |   .0502333   .0900982     0.56   0.577     -.126356    .2268226
              2  |  -.0231003   .0898566    -0.26   0.797    -.1992159    .1530153
                 |
           _cons |   .3087037   .0817324     3.78   0.000     .1485111    .4688964
----------------------------------------------------------------------------------

. 
. reg age i.immigrants_MetteF i.many_likes_immi i.comments_immi //model insignificant

      Source |       SS           df       MS      Number of obs   =     3,012
-------------+----------------------------------   F(4, 3007)      =      1.11
       Model |  1120.68225         4  280.170563   Prob > F        =    0.3477
    Residual |  755768.818     3,007  251.336488   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0002
       Total |  756889.501     3,011  251.374793   Root MSE        =    15.854

-------------------------------------------------------------------------------------
                age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
1.immigrants_MetteF |  -.8414639   .5778788    -1.46   0.145    -1.974542    .2916138
  1.many_likes_immi |   .6489523   .5779806     1.12   0.262     -.484325     1.78223
                    |
      comments_immi |
                 1  |   .3286518    .702791     0.47   0.640    -1.049348    1.706652
                 2  |   .7570655   .7098646     1.07   0.286    -.6348038    2.148935
                    |
              _cons |   47.45838   .6341313    74.84   0.000     46.21501    48.70176
-------------------------------------------------------------------------------------

. reg age i.vet_MetteF i.many_likes_vet i.comments_vet //model insignificant

      Source |       SS           df       MS      Number of obs   =     3,012
-------------+----------------------------------   F(4, 3007)      =      0.67
       Model |  677.672209         4  169.418052   Prob > F        =    0.6102
    Residual |  756211.828     3,007  251.483814   R-squared       =    0.0009
-------------+----------------------------------   Adj R-squared   =   -0.0004
       Total |  756889.501     3,011  251.374793   Root MSE        =    15.858

----------------------------------------------------------------------------------
             age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    1.vet_MetteF |   .3374333   .5780867     0.58   0.559     -.796052    1.470919
1.many_likes_vet |  -.4412758   .5782003    -0.76   0.445    -1.574984    .6924324
                 |
    comments_vet |
              1  |   .3257876     .70625     0.46   0.645    -1.058994     1.71057
              2  |   -.596198   .7061946    -0.84   0.399    -1.980871    .7884753
                 |
           _cons |   47.86318   .6410474    74.66   0.000     46.60625    49.12012
----------------------------------------------------------------------------------

. 
. reg LR_respondent i.immigrants_MetteF i.many_likes_immi i.comments_immi //model insignificant

      Source |       SS           df       MS      Number of obs   =     3,012
-------------+----------------------------------   F(4, 3007)      =      0.50
       Model |  12.1749851         4  3.04374628   Prob > F        =    0.7363
    Residual |  18332.3396     3,007  6.09655458   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =   -0.0007
       Total |  18344.5146     3,011  6.09249904   Root MSE        =    2.4691

-------------------------------------------------------------------------------------
      LR_respondent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
1.immigrants_MetteF |   .0488827   .0900018     0.54   0.587    -.1275886     .225354
  1.many_likes_immi |   .0371124   .0900177     0.41   0.680    -.1393901    .2136148
                    |
      comments_immi |
                 1  |  -.1070407   .1094563    -0.98   0.328    -.3216575     .107576
                 2  |    .020215   .1105579     0.18   0.855    -.1965619    .2369918
                    |
              _cons |   4.716559   .0987629    47.76   0.000     4.522909    4.910208
-------------------------------------------------------------------------------------

. reg LR_respondent i.vet_MetteF i.many_likes_vet i.comments_vet //model insignificant

      Source |       SS           df       MS      Number of obs   =     3,012
-------------+----------------------------------   F(4, 3007)      =      0.42
       Model |  10.1791715         4  2.54479287   Prob > F        =    0.7962
    Residual |  18334.3354     3,007   6.0972183   R-squared       =    0.0006
-------------+----------------------------------   Adj R-squared   =   -0.0008
       Total |  18344.5146     3,011  6.09249904   Root MSE        =    2.4693

----------------------------------------------------------------------------------
   LR_respondent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    1.vet_MetteF |  -.0367884   .0900127    -0.41   0.683    -.2132811    .1397043
1.many_likes_vet |  -.0984282   .0900304    -1.09   0.274    -.2749556    .0780992
                 |
    comments_vet |
              1  |   -.047162   .1099688    -0.43   0.668    -.2627836    .1684596
              2  |   .0124342   .1099601     0.11   0.910    -.2031705    .2280388
                 |
           _cons |   4.809834   .0998162    48.19   0.000     4.614119    5.005549
----------------------------------------------------------------------------------

. 
. 
. 
. 
. 
. ***Experimental results**********************************************************************************
. 
. ***Party sympathy***
. pwcorr PS_A PS_V, sig obs //Sympathy for the two parties are weakly correlated

             |     PS_A     PS_V
-------------+------------------
        PS_A |   1.0000 
             |
             |     2803
             |
        PS_V |  -0.1062   1.0000 
             |   0.0000
             |     2776     2802
             |

. 
. ***Liking***
.         *Vets
.         logit like_vet_both_yes i.vet_MetteF##c.PS_A  // i.vet_MetteF##c.PS_V i.many_likes_vet i.comments_vet

Iteration 0:   log likelihood = -1421.0053  
Iteration 1:   log likelihood = -1395.4427  
Iteration 2:   log likelihood = -1395.0614  
Iteration 3:   log likelihood = -1395.0614  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =      51.89
                                                Prob > chi2       =     0.0000
Log likelihood = -1395.0614                     Pseudo R2         =     0.0183

-----------------------------------------------------------------------------------
like_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     1.vet_MetteF |  -.9354334   .2410389    -3.88   0.000    -1.407861   -.4630057
             PS_A |  -.0421813   .0280133    -1.51   0.132    -.0970864    .0127238
                  |
vet_MetteF#c.PS_A |
               1  |    .215397    .039491     5.45   0.000     .1379961    .2927979
                  |
            _cons |  -1.273545   .1634885    -7.79   0.000    -1.593976   -.9531134
-----------------------------------------------------------------------------------

.         eststo m1a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(vet_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =    2.947367

2._at        : vet_MetteF      =           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |    .154668   .0137264
          2  |   .3004885   .0176676
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .1458205   .0228131     6.39   0.000     .1011077    .1905332
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =    2.947367

2._at        : vet_MetteF      =           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .1981545   .0151796            A
          2  |   .1671856   .0140277            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   -.030969   .0205477    -1.51   0.132    -.0712417    .0093038
------------------------------------------------------------------------------

.         estimates restore m1a 
(results m1a are active now)

.         margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0989471   .0157914     6.27   0.000     .0679966    .1298976
          2  |   .1154989   .0154791     7.46   0.000     .0851604    .1458374
          3  |   .1344065   .0147597     9.11   0.000      .105478    .1633349
          4  |   .1558638   .0136611    11.41   0.000     .1290886     .182639
          5  |   .1800342   .0123655    14.56   0.000     .1557982    .2042702
          6  |   .2070336   .0113763    18.20   0.000     .1847365    .2293306
          7  |    .236912   .0116276    20.38   0.000     .2141224    .2597016
          8  |   .2696363   .0139546    19.32   0.000     .2422857    .2969869
          9  |   .3050735   .0183051    16.67   0.000     .2691962    .3409509
         10  |   .3429811   .0241011    14.23   0.000     .2957437    .3902184
         11  |   .3830029   .0307858    12.44   0.000     .3226638     .443342
------------------------------------------------------------------------------

.         eststo like_vet_ps_a_mf

.         estimates restore m1a 
(results m1a are active now)

.         margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .218651   .0279308     7.83   0.000     .1639076    .2733944
          2  |   .2115302   .0231275     9.15   0.000     .1662011    .2568593
          3  |   .2045806   .0187651    10.90   0.000     .1678017    .2413595
          4  |    .197802   .0150003    13.19   0.000     .1684019    .2272021
          5  |    .191194   .0121222    15.77   0.000     .1674349    .2149531
          6  |    .184756   .0105681    17.48   0.000     .1640428    .2054692
          7  |   .1784869   .0106448    16.77   0.000     .1576234    .1993504
          8  |   .1723855   .0120951    14.25   0.000     .1486796    .1960914
          9  |   .1664504   .0143341    11.61   0.000     .1383561    .1945448
         10  |     .16068    .016914     9.50   0.000     .1275293    .1938308
         11  |   .1550725   .0195902     7.92   0.000     .1166764    .1934686
------------------------------------------------------------------------------

.         eststo like_vet_ps_a_ll

.         coefplot        (like_vet_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// ) //
> /
>                                 (like_vet_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Propability of" "Liking the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                 ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(like_vet_ps_A, replace) fxsize(100)

.         logit like_vet_both_yes i.vet_MetteF##c.PS_V 

Iteration 0:   log likelihood = -1423.4842  
Iteration 1:   log likelihood = -1389.9626  
Iteration 2:   log likelihood = -1389.0822  
Iteration 3:   log likelihood = -1389.0807  
Iteration 4:   log likelihood = -1389.0807  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =      68.81
                                                Prob > chi2       =     0.0000
Log likelihood = -1389.0807                     Pseudo R2         =     0.0242

-----------------------------------------------------------------------------------
like_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     1.vet_MetteF |    1.09759   .1751442     6.27   0.000     .7543142    1.440867
             PS_V |   .1902678   .0248396     7.66   0.000     .1415831    .2389525
                  |
vet_MetteF#c.PS_V |
               1  |  -.1960645   .0336797    -5.82   0.000    -.2620755   -.1300535
                  |
            _cons |  -2.303633   .1376082   -16.74   0.000     -2.57334   -2.033926
-----------------------------------------------------------------------------------

.         estimates store m1b

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(vet_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =    1.023965

2._at        : vet_MetteF      =           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .2293512   .0159936            A
          2  |   .2236215   .0156028            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0057296   .0224837    -0.25   0.799    -.0497968    .0383375
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =    1.023965

2._at        : vet_MetteF      =           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .1082439   .0112528
          2  |   .2620658   .0165684
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .1538219   .0194191     7.92   0.000     .1157611    .1918826
------------------------------------------------------------------------------

.         estimates restore m1b 
(results m1b are active now)

.         margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .230402   .0192121    11.99   0.000      .192747    .2680569
          2  |   .2293757   .0160638    14.28   0.000     .1978913    .2608601
          3  |   .2283527   .0134342    17.00   0.000     .2020221    .2546833
          4  |   .2273329   .0116531    19.51   0.000     .2044933    .2501725
          5  |   .2263163   .0111079    20.37   0.000     .2045453    .2480873
          6  |   .2253029   .0119433    18.86   0.000     .2018945    .2487114
          7  |   .2242928   .0138913    16.15   0.000     .1970664    .2515192
          8  |   .2232859   .0165457    13.50   0.000     .1908569    .2557149
          9  |   .2222822   .0196068    11.34   0.000     .1838536    .2607107
         10  |   .2212817   .0228992     9.66   0.000     .1764002    .2661632
         11  |   .2202844   .0263251     8.37   0.000     .1686883    .2718806
------------------------------------------------------------------------------

.         eststo like_vet_ps_v_mf

.         estimates restore m1b 
(results m1b are active now)

.         margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0908225   .0113628     7.99   0.000     .0685518    .1130932
          2  |   .1078045   .0112581     9.58   0.000     .0857391      .12987
          3  |   .1275165   .0109568    11.64   0.000     .1060416    .1489915
          4  |    .150226   .0106281    14.13   0.000     .1293953    .1710566
          5  |   .1761631   .0106625    16.52   0.000     .1552649    .1970614
          6  |   .2054955   .0116575    17.63   0.000     .1826473    .2283438
          7  |   .2382993   .0140981    16.90   0.000     .2106676     .265931
          8  |   .2745301   .0180362    15.22   0.000     .2391798    .3098804
          9  |   .3139987   .0232032    13.53   0.000     .2685212    .3594762
         10  |   .3563543   .0292296    12.19   0.000     .2990654    .4136432
         11  |   .4010829   .0357191    11.23   0.000     .3310747     .471091
------------------------------------------------------------------------------

.         eststo like_vet_ps_v_ll

.         coefplot        (like_vet_ps_v_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// ) //
> /
>                                 (like_vet_ps_v_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                 ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(like_vet_ps_V, replace) fxsize(90)

.         *Immigrants
.         logit like_immi_both_yes i.immigrants_MetteF##c.PS_A   

Iteration 0:   log likelihood = -1059.2446  
Iteration 1:   log likelihood = -1029.6106  
Iteration 2:   log likelihood = -1028.2701  
Iteration 3:   log likelihood = -1028.2686  
Iteration 4:   log likelihood = -1028.2686  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =      61.95
                                                Prob > chi2       =     0.0000
Log likelihood = -1028.2686                     Pseudo R2         =     0.0292

------------------------------------------------------------------------------------------
      like_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |   -1.30943   .2982367    -4.39   0.000    -1.893963    -.724897
                    PS_A |   -.095395   .0348112    -2.74   0.006    -.1636238   -.0271662
                         |
immigrants_MetteF#c.PS_A |
                      1  |   .3071427   .0489256     6.28   0.000     .2112504    .4030351
                         |
                   _cons |  -1.662463    .194795    -8.53   0.000    -2.044254   -1.280671
------------------------------------------------------------------------------------------

.         eststo m2a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0872412   .0107479
          2  |    .213415   .0161634
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .1261738   .0198212     6.37   0.000     .0873249    .1650227
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //significant, but small in magnitude

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |     .12525   .0123825
          2  |   .0821369   .0100331
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0431131    .015659    -2.75   0.006    -.0738042   -.0124219
------------------------------------------------------------------------------

.         estimates restore m2a   
(results m2a are active now)

.         margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0487119   .0104648     4.65   0.000     .0282012    .0692226
          2  |   .0595162   .0108503     5.49   0.000     .0382501    .0807824
          3  |   .0725342   .0109544     6.62   0.000     .0510639    .0940045
          4  |   .0881328   .0107277     8.22   0.000     .0671069    .1091587
          5  |   .1066999   .0102151    10.45   0.000     .0866787    .1267211
          6  |   .1286269   .0097174    13.24   0.000     .1095812    .1476726
          7  |   .1542817   .0100319    15.38   0.000     .1346195    .1739438
          8  |    .183973   .0122607    15.01   0.000     .1599425    .2080035
          9  |    .217906   .0168641    12.92   0.000     .1848529    .2509591
         10  |   .2561334   .0235502    10.88   0.000     .2099759     .302291
         11  |   .2985074   .0318453     9.37   0.000     .2360918    .3609231
------------------------------------------------------------------------------

.         eststo like_immi_ps_a_mf

.         estimates restore m2a
(results m2a are active now)

.         margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1594317   .0261051     6.11   0.000     .1082666    .2105968
          2  |   .1470589   .0206277     7.13   0.000     .1066293    .1874885
          3  |   .1354915   .0159611     8.49   0.000     .1042082    .1667747
          4  |   .1247009   .0122107    10.21   0.000     .1007684    .1486333
          5  |   .1146557   .0095686    11.98   0.000     .0959017    .1334098
          6  |   .1053224   .0082577    12.75   0.000     .0891377    .1215071
          7  |   .0966658   .0082367    11.74   0.000     .0805222    .1128095
          8  |   .0886503   .0090468     9.80   0.000     .0709189    .1063817
          9  |   .0812396   .0101806     7.98   0.000      .061286    .1011932
         10  |   .0743978   .0113347     6.56   0.000     .0521821    .0966135
         11  |   .0680896   .0123702     5.50   0.000     .0438444    .0923347
------------------------------------------------------------------------------

.         eststo like_immi_ps_a_ll

.         coefplot        (like_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// ) /
> //
>                                 (like_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Liking the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(like_immi_ps_A, replace) fxsize(100)               

.         logit like_immi_both_yes i.immigrants_MetteF##c.PS_V 

Iteration 0:   log likelihood = -1059.1103  
Iteration 1:   log likelihood = -1010.6455  
Iteration 2:   log likelihood = -1006.4461  
Iteration 3:   log likelihood = -1006.4362  
Iteration 4:   log likelihood = -1006.4362  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =     105.35
                                                Prob > chi2       =     0.0000
Log likelihood = -1006.4362                     Pseudo R2         =     0.0497

------------------------------------------------------------------------------------------
      like_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |   1.675839   .2474117     6.77   0.000     1.190921    2.160757
                    PS_V |   .2961696    .033067     8.96   0.000     .2313594    .3609798
                         |
immigrants_MetteF#c.PS_V |
                      1  |  -.2544975   .0425411    -5.98   0.000    -.3378766   -.1711184
                         |
                   _cons |  -3.586968   .2090165   -17.16   0.000    -3.996632   -3.177303
------------------------------------------------------------------------------------------

.         estimates store m2b

.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         *Comparing respondents -1sd and +1sd
.         margins, at(immigrants_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .1337203   .0128531            A
          2  |   .1633746   .0140241            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0296542   .0190088     1.56   0.119    -.0076023    .0669108
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0361346   .0062392
          2  |   .1662245   .0133762
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .1300899   .0136386     9.54   0.000     .1033587     .156821
------------------------------------------------------------------------------

.         estimates restore m2b
(results m2b are active now)

.         margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1288542   .0148598     8.67   0.000     .0997294    .1579789
          2  |   .1336047   .0128983    10.36   0.000     .1083246    .1588848
          3  |   .1385025   .0111551    12.42   0.000     .1166389    .1603661
          4  |   .1435501   .0099097    14.49   0.000     .1241274    .1629728
          5  |   .1487499   .0095484    15.58   0.000     .1300353    .1674645
          6  |   .1541042   .0103536    14.88   0.000     .1338114    .1743969
          7  |    .159615   .0122669    13.01   0.000     .1355723    .1836578
          8  |   .1652844   .0150189    11.01   0.000      .135848    .1947209
          9  |   .1711142   .0183676     9.32   0.000     .1351144    .2071141
         10  |    .177106   .0221615     7.99   0.000     .1336703    .2205418
         11  |   .1832612   .0263142     6.96   0.000     .1316863    .2348362
------------------------------------------------------------------------------

.         eststo like_immi_ps_a_mf

.         estimates restore m2b
(results m2b are active now)

.         margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0269365   .0054785     4.92   0.000     .0161988    .0376742
          2  |   .0358882   .0062219     5.77   0.000     .0236935    .0480829
          3  |   .0476691   .0069103     6.90   0.000     .0341252    .0612131
          4  |   .0630644   .0075189     8.39   0.000     .0483276    .0778011
          5  |   .0829983   .0081268    10.21   0.000     .0670702    .0989265
          6  |   .1085035   .0090725    11.96   0.000     .0907217    .1262852
          7  |   .1406442   .0110636    12.71   0.000     .1189599    .1623285
          8  |   .1803792   .0148936    12.11   0.000     .1511884    .2095701
          9  |   .2283572   .0209272    10.91   0.000     .1873408    .2693737
         10  |   .2846644   .0289753     9.82   0.000     .2278738     .341455
         11  |   .3485835   .0383863     9.08   0.000     .2733478    .4238192
------------------------------------------------------------------------------

.         eststo like_immi_ps_a_ll

.         coefplot        (like_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// ) /
> //
>                                 (like_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(like_immi_ps_V, replace)   fxsize(90)              

.         graph close

.         *FIGURE 1: Liking as a Function of Party Sympathy
.         grc1leg2  like_vet_ps_A like_vet_ps_V like_immi_ps_A like_immi_ps_V     , ycommon pos(12) title("" , size(medlarge)) note("Note: Estimates with 95% confidence intervals (n=2,802/2,803)")

.         graph export FIGURE_1.png, replace              
(file FIGURE_1.png written in PNG format)

. 
.                 
. ***Commenting***
.         *Vets
.         logit comment_vet_both_yes i.vet_MetteF##c.PS_A

Iteration 0:   log likelihood = -345.55935  
Iteration 1:   log likelihood = -343.71065  
Iteration 2:   log likelihood = -343.66722  
Iteration 3:   log likelihood = -343.66719  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =       3.78
                                                Prob > chi2       =     0.2857
Log likelihood = -343.66719                     Pseudo R2         =     0.0055

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |   -.789591   .6143198    -1.29   0.199    -1.993636    .4144536
                PS_A |  -.0016796   .0673574    -0.02   0.980    -.1336978    .1303385
                     |
   vet_MetteF#c.PS_A |
                  1  |   .1368739   .0979081     1.40   0.162    -.0550224    .3287702
                     |
               _cons |  -3.583354   .4020923    -8.91   0.000    -4.371441   -2.795268
--------------------------------------------------------------------------------------

.         eststo m3a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(vet_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //marginally significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =    2.947367

2._at        : vet_MetteF      =           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0184426    .005045            A
          2  |   .0352861   .0070425            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0168435   .0087377     1.93   0.054    -.0002821    .0339691
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =    2.947367

2._at        : vet_MetteF      =           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0269015   .0061835            A
          2  |   .0266857   .0060816            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0002158   .0086545    -0.02   0.980    -.0171783    .0167467
------------------------------------------------------------------------------

.         estimates restore m3a
(results m3a are active now)

.         margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0124569   .0057135     2.18   0.029     .0012587    .0236551
          2  |   .0142345   .0055959     2.54   0.011     .0032667    .0252023
          3  |   .0162615    .005365     3.03   0.002     .0057464    .0267767
          4  |   .0185718   .0050249     3.70   0.000     .0087233    .0284204
          5  |   .0212033   .0046234     4.59   0.000     .0121417    .0302649
          6  |   .0241984   .0043123     5.61   0.000     .0157465    .0326503
          7  |   .0276046   .0044248     6.24   0.000     .0189321    .0362771
          8  |   .0314749   .0053754     5.86   0.000     .0209393    .0420104
          9  |   .0358677   .0073449     4.88   0.000      .021472    .0502634
         10  |   .0408478   .0103085     3.96   0.000     .0206435    .0610521
         11  |    .046486   .0142559     3.26   0.001     .0185449    .0744271
------------------------------------------------------------------------------

.         eststo comment_vet_ps_a_mf

.         estimates restore m3a 
(results m3a are active now)

.         margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0270314   .0105753     2.56   0.011     .0063042    .0477585
          2  |   .0269872   .0089788     3.01   0.003     .0093891    .0445853
          3  |   .0269431   .0074723     3.61   0.000     .0122978    .0415885
          4  |   .0268991   .0061176     4.40   0.000     .0149089    .0388893
          5  |   .0268552   .0050327     5.34   0.000     .0169913    .0367191
          6  |   .0268113   .0044142     6.07   0.000     .0181596    .0354631
          7  |   .0267676    .004454     6.01   0.000     .0180379    .0354972
          8  |   .0267238    .005131     5.21   0.000     .0166674    .0367803
          9  |   .0266802   .0062364     4.28   0.000      .014457    .0389034
         10  |   .0266366   .0075815     3.51   0.000     .0117771    .0414961
         11  |   .0265931   .0090569     2.94   0.003     .0088419    .0443443
------------------------------------------------------------------------------

.         eststo comment_vet_ps_a_ll

.         coefplot        (comment_vet_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// )
>  ///
>                                 (comment_vet_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_vet_ps_A, replace) fxsize(100)

.         logit comment_vet_both_yes i.vet_MetteF##c.PS_V

Iteration 0:   log likelihood = -345.53222  
Iteration 1:   log likelihood = -344.92809  
Iteration 2:   log likelihood =  -344.9255  
Iteration 3:   log likelihood =  -344.9255  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =       1.21
                                                Prob > chi2       =     0.7498
Log likelihood =  -344.9255                     Pseudo R2         =     0.0018

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |  -.0359866    .414781    -0.09   0.931    -.8489424    .7769692
                PS_V |   .0416462   .0575381     0.72   0.469    -.0711263    .1544188
                     |
   vet_MetteF#c.PS_V |
                  1  |   .0069029   .0817086     0.08   0.933    -.1532431    .1670489
                     |
               _cons |  -3.756745   .2900504   -12.95   0.000    -4.325233   -3.188256
--------------------------------------------------------------------------------------

.         eststo m3b

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(vet_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =    1.023965

2._at        : vet_MetteF      =           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0231346   .0056332            A
          2  |   .0302046   .0063275            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |     .00707   .0084072     0.84   0.400    -.0094078    .0235478
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =    1.023965

2._at        : vet_MetteF      =           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0237972   .0056728            A
          2  |   .0299122   .0064333            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |    .006115    .008443     0.72   0.469    -.0104331     .022663
------------------------------------------------------------------------------

.         estimates restore m3b
(results m3b are active now)

.         margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0220374   .0063902     3.45   0.001     .0095129    .0345619
          2  |   .0231083   .0056506     4.09   0.000     .0120334    .0341833
          3  |   .0242301   .0049666     4.88   0.000     .0144958    .0339643
          4  |   .0254048    .004452     5.71   0.000     .0166791    .0341305
          5  |    .026635    .004281     6.22   0.000     .0182444    .0350255
          6  |    .027923    .004617     6.05   0.000     .0188739     .036972
          7  |   .0292714     .00549     5.33   0.000     .0185112    .0400316
          8  |   .0306829    .006812     4.50   0.000     .0173316    .0440341
          9  |   .0321602   .0084854     3.79   0.000      .015529    .0487913
         10  |   .0337061   .0104484     3.23   0.001     .0132276    .0541846
         11  |   .0353236   .0126694     2.79   0.005     .0104921    .0601552
------------------------------------------------------------------------------

.         eststo comment_vet_ps_v_mf

.         estimates restore m3b           
(results m3b are active now)

.         margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0228264   .0064697     3.53   0.000     .0101461    .0355068
          2  |   .0237741   .0056908     4.18   0.000     .0126202    .0349279
          3  |     .02476    .004992     4.96   0.000     .0149759    .0345442
          4  |   .0257858   .0044921     5.74   0.000     .0169815    .0345902
          5  |   .0268529   .0043597     6.16   0.000      .018308    .0353979
          6  |   .0279629   .0047325     5.91   0.000     .0186875    .0372384
          7  |   .0291175   .0056124     5.19   0.000     .0181174    .0401176
          8  |   .0303182   .0069032     4.39   0.000     .0167881    .0438482
          9  |   .0315668   .0085093     3.71   0.000     .0148888    .0482447
         10  |    .032865   .0103701     3.17   0.002       .01254      .05319
         11  |   .0342148   .0124533     2.75   0.006     .0098068    .0586229
------------------------------------------------------------------------------

.         eststo comment_vet_ps_v_ll

.         coefplot        (comment_vet_ps_v_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// )
>  ///
>                                 (comment_vet_ps_v_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_vet_ps_V, replace) fxsize(90)

.         *Immigrants
.         logit comment_immi_both_yes i.immigrants_MetteF##c.PS_A

Iteration 0:   log likelihood =  -418.2503  
Iteration 1:   log likelihood = -414.41636  
Iteration 2:   log likelihood = -414.28534  
Iteration 3:   log likelihood = -414.28521  
Iteration 4:   log likelihood = -414.28521  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =       7.93
                                                Prob > chi2       =     0.0475
Log likelihood = -414.28521                     Pseudo R2         =     0.0095

------------------------------------------------------------------------------------------
   comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |  -1.242555   .5614201    -2.21   0.027    -2.342918    -.142192
                    PS_A |  -.0158326   .0533547    -0.30   0.767     -.120406    .0887407
                         |
immigrants_MetteF#c.PS_A |
                      1  |   .1375529   .0896381     1.53   0.125    -.0381346    .3132403
                         |
                   _cons |  -3.048176   .3162175    -9.64   0.000     -3.66795   -2.428401
------------------------------------------------------------------------------------------

.         eststo m4a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0192279   .0052151            A
          2  |   .0344819   .0071306            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0152539   .0089616     1.70   0.089    -.0023105    .0328183
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0433206   .0076971            A
          2  |   .0402002   .0072643            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0031203   .0105211    -0.30   0.767    -.0237412    .0175005
------------------------------------------------------------------------------

.         estimates restore m4a
(results m4a are active now)

.         margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0135099   .0061825     2.19   0.029     .0013924    .0256274
          2  |    .015232   .0059629     2.55   0.011     .0035449     .026919
          3  |   .0171697   .0056281     3.05   0.002     .0061388    .0282006
          4  |   .0193491   .0051901     3.73   0.000     .0091766    .0295216
          5  |    .021799   .0047095     4.63   0.000     .0125685    .0310295
          6  |   .0245514    .004358     5.63   0.000     .0160099    .0330929
          7  |   .0276414   .0044835     6.17   0.000     .0188539    .0364289
          8  |    .031108   .0054682     5.69   0.000     .0203904    .0418255
          9  |   .0349936   .0074277     4.71   0.000     .0204355    .0495517
         10  |   .0393449   .0102974     3.82   0.000     .0191625    .0595274
         11  |   .0442125   .0140434     3.15   0.002     .0166879    .0717371
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_mf

.         estimates restore m4a 
(results m4a are active now)

.         margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0452963   .0136747     3.31   0.001     .0184944    .0720982
          2  |   .0446165   .0114557     3.89   0.000     .0221638    .0670693
          3  |   .0439465   .0094092     4.67   0.000     .0255047    .0623883
          4  |   .0432861   .0076105     5.69   0.000     .0283698    .0582024
          5  |   .0426351   .0062018     6.87   0.000     .0304797    .0547905
          6  |   .0419935   .0054113     7.76   0.000     .0313875    .0525996
          7  |   .0413612   .0054356     7.61   0.000     .0307076    .0520148
          8  |    .040738   .0062019     6.57   0.000     .0285825    .0528934
          9  |   .0401237   .0074357     5.40   0.000     .0255501    .0546973
         10  |   .0395184    .008906     4.44   0.000     .0220628    .0569739
         11  |   .0389218   .0104825     3.71   0.000     .0183764    .0594671
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_ll

.         coefplot        (comment_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// 
> ) ///
>                                 (comment_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_immi_ps_A, replace)        fxsize(100)             

.         logit comment_immi_both_yes i.immigrants_MetteF##c.PS_V

Iteration 0:   log likelihood = -418.21544  
Iteration 1:   log likelihood = -414.59599  
Iteration 2:   log likelihood =  -414.4831  
Iteration 3:   log likelihood = -414.48301  
Iteration 4:   log likelihood = -414.48301  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =       7.46
                                                Prob > chi2       =     0.0585
Log likelihood = -414.48301                     Pseudo R2         =     0.0089

------------------------------------------------------------------------------------------
   comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |  -.8802613    .363598    -2.42   0.015      -1.5929   -.1676223
                    PS_V |   -.066111   .0484974    -1.36   0.173    -.1611642    .0289422
                         |
immigrants_MetteF#c.PS_V |
                      1  |   .1084083   .0763232     1.42   0.155    -.0411824     .257999
                         |
                   _cons |  -2.890804    .211011   -13.70   0.000    -3.304378    -2.47723
------------------------------------------------------------------------------------------

.         eststo m4b      

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0234821   .0056995            A
          2  |   .0296239   .0064094            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0061418   .0085416     0.72   0.472    -.0105993     .022883
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0493363   .0082218            A
          2  |   .0345056   .0069221            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0148307   .0108185    -1.37   0.170    -.0360345    .0063732
------------------------------------------------------------------------------

.         estimates restore m4b
(results m4b are active now)

.         margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0225092    .006515     3.45   0.001     .0097399    .0352784
          2  |   .0234589   .0057181     4.10   0.000     .0122516    .0346661
          3  |   .0244476   .0049956     4.89   0.000     .0146564    .0342389
          4  |    .025477   .0044684     5.70   0.000      .016719    .0342349
          5  |   .0265485   .0043133     6.16   0.000     .0180945    .0350024
          6  |   .0276637   .0046796     5.91   0.000      .018492    .0368355
          7  |   .0288245   .0055723     5.17   0.000      .017903     .039746
          8  |   .0300324    .006889     4.36   0.000     .0165302    .0435347
          9  |   .0312894   .0085288     3.67   0.000     .0145732    .0480056
         10  |   .0325971    .010429     3.13   0.002     .0121567    .0530376
         11  |   .0339577   .0125567     2.70   0.007     .0093469    .0585684
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_mf

.         estimates restore m4b 
(results m4b are active now)

.         margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     .05261   .0105173     5.00   0.000     .0319966    .0732235
          2  |   .0494107   .0082692     5.98   0.000     .0332034     .065618
          3  |   .0463964   .0065876     7.04   0.000     .0334849    .0593079
          4  |   .0435576   .0055966     7.78   0.000     .0325883    .0545268
          5  |    .040885   .0053522     7.64   0.000     .0303949     .051375
          6  |   .0383698    .005706     6.72   0.000     .0271862    .0495533
          7  |   .0360035   .0063907     5.63   0.000     .0234779    .0485291
          8  |   .0337781   .0071949     4.69   0.000     .0196764    .0478798
          9  |   .0316857   .0080006     3.96   0.000     .0160048    .0473665
         10  |   .0297189    .008751     3.40   0.001     .0125673    .0468704
         11  |   .0278707   .0094209     2.96   0.003     .0094061    .0463353
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_ll

.         coefplot        (comment_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// 
> ) ///
>                                 (comment_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_immi_ps_V, replace)        fxsize(90)              

.         graph close

.         *FIGURE 2: Commenting as a Function of Party Sympathy
.         grc1leg2  comment_vet_ps_A comment_vet_ps_V comment_immi_ps_A comment_immi_ps_V , xsize(6) ycommon pos(12) title("", size(medlarge)) note("Note: Estimates with 95% confidence intervals (n=2,802/2,
> 803)")

.         graph export FIGURE_2.png, replace      
(file FIGURE_2.png written in PNG format)

.         
.                 
. ***Sharing***
.         *Vets
.         logit share_vet_both_yes i.vet_MetteF##c.PS_A  

Iteration 0:   log likelihood = -476.70586  
Iteration 1:   log likelihood = -466.72572  
Iteration 2:   log likelihood = -465.76019  
Iteration 3:   log likelihood =   -465.758  
Iteration 4:   log likelihood =   -465.758  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =      21.90
                                                Prob > chi2       =     0.0001
Log likelihood =   -465.758                     Pseudo R2         =     0.0230

------------------------------------------------------------------------------------
share_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
      1.vet_MetteF |  -.8734358    .511098    -1.71   0.087    -1.875169    .1282979
              PS_A |  -.0498768   .0626137    -0.80   0.426    -.1725974    .0728438
                   |
 vet_MetteF#c.PS_A |
                1  |   .2437456   .0827138     2.95   0.003     .0816295    .4058617
                   |
             _cons |   -3.19776   .3586826    -8.92   0.000    -3.900765   -2.494755
------------------------------------------------------------------------------------

.         eststo m5a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(vet_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) // Significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =    2.947367

2._at        : vet_MetteF      =           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0293181   .0062737
          2  |   .0727932   .0099112
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0434751   .0117916     3.69   0.000     .0203639    .0665862
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =    2.947367

2._at        : vet_MetteF      =           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |    .034067   .0068737            A
          2  |   .0268435   .0060488            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0072235   .0090585    -0.80   0.425    -.0249779    .0105309
------------------------------------------------------------------------------

.         estimates restore m5a
(results m5a are active now)

.         margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0167709   .0060039     2.79   0.005     .0050036    .0285383
          2  |   .0202861   .0062341     3.25   0.001     .0080675    .0325046
          3  |   .0245196   .0063307     3.87   0.000     .0121117    .0369275
          4  |   .0296099   .0062663     4.73   0.000     .0173283    .0418915
          5  |   .0357183   .0060554     5.90   0.000       .02385    .0475866
          6  |   .0430309   .0058382     7.37   0.000     .0315883    .0544736
          7  |   .0517604   .0060379     8.57   0.000     .0399264    .0635944
          8  |   .0621456   .0073744     8.43   0.000     .0476922    .0765991
          9  |   .0744511    .010388     7.17   0.000     .0540909    .0948112
         10  |   .0889619   .0152244     5.84   0.000     .0591226    .1188012
         11  |   .1059772   .0219532     4.83   0.000     .0629496    .1490047
------------------------------------------------------------------------------

.         eststo share_vet_ps_a_mf

.         estimates restore m5a 
(results m5a are active now)

.         margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0392501   .0135258     2.90   0.004     .0127401    .0657601
          2  |   .0374119   .0109333     3.42   0.001     .0159831    .0588408
          3  |   .0356566    .008667     4.11   0.000     .0186695    .0526437
          4  |   .0339807   .0067859     5.01   0.000     .0206807    .0472808
          5  |    .032381   .0054066     5.99   0.000     .0217842    .0429778
          6  |   .0308542   .0046956     6.57   0.000      .021651    .0400573
          7  |   .0293971   .0047216     6.23   0.000      .020143    .0386512
          8  |   .0280069    .005303     5.28   0.000     .0176132    .0384006
          9  |   .0266806   .0061647     4.33   0.000      .014598    .0387633
         10  |   .0254155   .0071189     3.57   0.000     .0114628    .0393682
         11  |   .0242089   .0080695     3.00   0.003      .008393    .0400248
------------------------------------------------------------------------------

.         eststo share_vet_ps_a_ll

.         coefplot        (share_vet_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// ) /
> //
>                                 (share_vet_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Sharing the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).12,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(share_vet_ps_A, replace) fxsize(100)

.         logit share_vet_both_yes i.vet_MetteF##c.PS_V 

Iteration 0:   log likelihood = -476.66433  
Iteration 1:   log likelihood = -468.42117  
Iteration 2:   log likelihood = -467.99981  
Iteration 3:   log likelihood = -467.99852  
Iteration 4:   log likelihood = -467.99852  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =      17.33
                                                Prob > chi2       =     0.0006
Log likelihood = -467.99852                     Pseudo R2         =     0.0182

------------------------------------------------------------------------------------
share_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
      1.vet_MetteF |    1.12641   .3828825     2.94   0.003     .3759739    1.876846
              PS_V |    .164301   .0540989     3.04   0.002     .0582691    .2703329
                   |
 vet_MetteF#c.PS_V |
                1  |  -.1308725   .0690259    -1.90   0.058    -.2661609    .0044159
                   |
             _cons |   -4.19154   .3173131   -13.21   0.000    -4.813462   -3.569618
------------------------------------------------------------------------------------

.         eststo m5b      

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(vet_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // Not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =    1.023965

2._at        : vet_MetteF      =           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0460493   .0078964            A
          2  |   .0550814   .0084732            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0090321   .0115521     0.78   0.434    -.0136096    .0316739
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // Significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =    1.023965

2._at        : vet_MetteF      =           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0175792   .0046733
          2  |   .0432615   .0074006
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0256823   .0082472     3.11   0.002     .0095181    .0418464
------------------------------------------------------------------------------

.         estimates restore m5b
(results m5b are active now)

.         margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0445687   .0091241     4.88   0.000     .0266858    .0624516
          2  |   .0460141   .0079241     5.81   0.000     .0304832     .061545
          3  |   .0475039   .0068526     6.93   0.000     .0340731    .0609348
          4  |   .0490396   .0060737     8.07   0.000     .0371353    .0609439
          5  |   .0506222   .0058199     8.70   0.000     .0392154    .0620291
          6  |   .0522531   .0062725     8.33   0.000     .0399593     .064547
          7  |   .0539336   .0074111     7.28   0.000     .0394081     .068459
          8  |   .0556649   .0090782     6.13   0.000     .0378719    .0734579
          9  |   .0574485   .0111275     5.16   0.000      .035639    .0792579
         10  |   .0592856   .0134674     4.40   0.000       .03289    .0856812
         11  |   .0611776   .0160477     3.81   0.000     .0297247    .0926305
------------------------------------------------------------------------------

.         eststo share_vet_ps_v_mf

.         estimates restore m5b 
(results m5b are active now)

.         margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0148977   .0046568     3.20   0.001     .0057705    .0240248
          2  |   .0175114   .0046737     3.75   0.000      .008351    .0266717
          3  |    .020574   .0046284     4.45   0.000     .0115025    .0296456
          4  |   .0241591   .0045698     5.29   0.000     .0152025    .0331158
          5  |   .0283509   .0046293     6.12   0.000     .0192775    .0374243
          6  |   .0332452   .0050577     6.57   0.000     .0233323     .043158
          7  |   .0389505   .0061667     6.32   0.000      .026864     .051037
          8  |   .0455888   .0081827     5.57   0.000      .029551    .0616265
          9  |   .0532957   .0112198     4.75   0.000     .0313052    .0752861
         10  |   .0622205   .0153697     4.05   0.000     .0320963    .0923446
         11  |   .0725253   .0207515     3.49   0.000     .0318531    .1131975
------------------------------------------------------------------------------

.         eststo share_vet_ps_v_ll

.         coefplot        (share_vet_ps_v_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// ) /
> //
>                                 (share_vet_ps_v_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).12,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(share_vet_ps_V, replace) fxsize(90)

.         *Immigrants
.         logit share_immi_both_yes i.immigrants_MetteF##c.PS_A   

Iteration 0:   log likelihood = -359.82694  
Iteration 1:   log likelihood = -352.88804  
Iteration 2:   log likelihood = -352.20778  
Iteration 3:   log likelihood = -352.20641  
Iteration 4:   log likelihood = -352.20641  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =      15.24
                                                Prob > chi2       =     0.0016
Log likelihood = -352.20641                     Pseudo R2         =     0.0212

------------------------------------------------------------------------------------------
     share_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |  -1.221058   .6134802    -1.99   0.047    -2.423457   -.0186591
                    PS_A |  -.0660309   .0709622    -0.93   0.352    -.2051142    .0730525
                         |
immigrants_MetteF#c.PS_A |
                      1  |   .2832685   .0982997     2.88   0.004     .0906047    .4759324
                         |
                   _cons |  -3.417539   .4019374    -8.50   0.000    -4.205321   -2.629756
------------------------------------------------------------------------------------------

.         eststo m6a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) // Significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0180159   .0049733
          2  |   .0507898   .0085663
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0327738   .0099799     3.28   0.001     .0132135    .0523341
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) // Not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0262841   .0059875            A
          2  |   .0191239   .0050171            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0071602   .0076873    -0.93   0.352     -.022227    .0079066
------------------------------------------------------------------------------

.         estimates restore m6a
(results m6a are active now)

.         margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0095786   .0043969     2.18   0.029     .0009609    .0181963
          2  |   .0118752   .0046896     2.53   0.011     .0026837    .0210667
          3  |   .0147142   .0048928     3.01   0.003     .0051244     .024304
          4  |   .0182193   .0049742     3.66   0.000     .0084701    .0279686
          5  |   .0225404   .0049273     4.57   0.000      .012883    .0321978
          6  |   .0278573   .0048409     5.75   0.000     .0183692    .0373454
          7  |   .0343841   .0050514     6.81   0.000     .0244836    .0442846
          8  |   .0423736    .006233     6.80   0.000     .0301572      .05459
          9  |   .0521192   .0090146     5.78   0.000     .0344508    .0697876
         10  |   .0639566   .0136847     4.67   0.000     .0371351     .090778
         11  |   .0782605    .020462     3.82   0.000     .0381556    .1183653
------------------------------------------------------------------------------

.         eststo share_immi_ps_a_mf

.         estimates restore m6a 
(results m6a are active now)

.         margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0317518    .012357     2.57   0.010     .0075325    .0559711
          2  |   .0297834   .0098207     3.03   0.002     .0105352    .0490315
          3  |   .0279334   .0076573     3.65   0.000     .0129254    .0429415
          4  |   .0261953   .0059069     4.43   0.000     .0146181    .0377725
          5  |   .0245626   .0046572     5.27   0.000     .0154346    .0336906
          6  |   .0230292    .004026     5.72   0.000     .0151385      .03092
          7  |   .0215895    .004026     5.36   0.000     .0136986    .0294803
          8  |   .0202379   .0044649     4.53   0.000     .0114868    .0289889
          9  |   .0189692   .0051016     3.72   0.000     .0089702    .0289683
         10  |   .0177787   .0057831     3.07   0.002     .0064439    .0291134
         11  |   .0166616   .0064351     2.59   0.010      .004049    .0292742
------------------------------------------------------------------------------

.         eststo share_immi_ps_a_ll

.         coefplot        (share_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// ) 
> ///
>                                 (share_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Sharing the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).12,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(share_immi_ps_A, replace)  fxsize(100)             

.         logit share_immi_both_yes i.immigrants_MetteF##c.PS_V   

Iteration 0:   log likelihood = -359.79834  
Iteration 1:   log likelihood = -355.95199  
Iteration 2:   log likelihood = -355.78873  
Iteration 3:   log likelihood = -355.78839  
Iteration 4:   log likelihood = -355.78839  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =       8.02
                                                Prob > chi2       =     0.0456
Log likelihood = -355.78839                     Pseudo R2         =     0.0111

------------------------------------------------------------------------------------------
     share_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |   .6644779   .4358095     1.52   0.127     -.189693    1.518649
                    PS_V |   .1164678   .0611362     1.91   0.057     -.003357    .2362925
                         |
immigrants_MetteF#c.PS_V |
                      1  |  -.0563373   .0804626    -0.70   0.484    -.2140411    .1013665
                         |
                   _cons |  -4.263971   .3431284   -12.43   0.000     -4.93649   -3.591452
------------------------------------------------------------------------------------------

.         eststo m6b

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // Not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0282523   .0062043            A
          2  |   .0392166   .0073072            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0109643   .0095013     1.15   0.249    -.0076578    .0295865
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // Marginally significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0156008   .0044776            A
          2  |   .0296681    .006124            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0140673   .0072504     1.94   0.052    -.0001433    .0282779
------------------------------------------------------------------------------

.         estimate restore m6b
(results m6b are active now)

.         margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0266101   .0069595     3.82   0.000     .0129697    .0402506
          2  |   .0282128   .0062218     4.53   0.000     .0160183    .0404073
          3  |    .029909   .0055263     5.41   0.000     .0190776    .0407403
          4  |   .0317038   .0050003     6.34   0.000     .0219035    .0415041
          5  |   .0336026   .0048446     6.94   0.000     .0241075    .0430978
          6  |    .035611   .0052596     6.77   0.000     .0253023    .0459196
          7  |   .0377347   .0063025     5.99   0.000      .025382    .0500874
          8  |   .0399798   .0078913     5.07   0.000     .0245132    .0554465
          9  |   .0423526   .0099258     4.27   0.000     .0228984    .0618068
         10  |   .0448597   .0123428     3.63   0.000     .0206681    .0690512
         11  |   .0475078   .0151131     3.14   0.002     .0178866     .077129
------------------------------------------------------------------------------

.         eststo share_immi_ps_a_mf

.         estimates restore m6b 
(results m6b are active now)

.         margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0138712   .0046936     2.96   0.003      .004672    .0230705
          2  |    .015558   .0044833     3.47   0.001     .0067709     .024345
          3  |   .0174462   .0042375     4.12   0.000     .0091408    .0257515
          4  |    .019559   .0040229     4.86   0.000     .0116743    .0274436
          5  |    .021922   .0039758     5.51   0.000     .0141296    .0297143
          6  |   .0245633   .0043016     5.71   0.000     .0161324    .0329942
          7  |   .0275139   .0051853     5.31   0.000     .0173508     .037677
          8  |   .0308078   .0067003     4.60   0.000     .0176753    .0439402
          9  |    .034482   .0088481     3.90   0.000     .0171399     .051824
         10  |   .0385769   .0116332     3.32   0.001     .0157762    .0613776
         11  |   .0431364   .0150873     2.86   0.004     .0135659    .0727069
------------------------------------------------------------------------------

.         eststo share_immi_ps_a_ll

.         coefplot        (share_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// ) 
> ///
>                                 (share_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).12,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(share_immi_ps_V, replace) fxsize(90)                       

.         graph close

.         *FIGURE 3: Sharing as a function of Party Sympathy
.         grc1leg2  share_vet_ps_A share_vet_ps_V share_immi_ps_A share_immi_ps_V , ycommon pos(12) title("", size(medlarge)) note("Note: Estimates with 95% confidence intervals (n=2,802/2,803)")

.         graph export FIGURE_3.png, replace      
(file FIGURE_3.png written in PNG format)

. 
. 
.         *Robustness: Models where sympathy is treated as categorical
.                 
.                 ***Liking***
.                 
.                 *Vets
.                 logit like_vet_both_yes i.vet_MetteF##i.PS_A

Iteration 0:   log likelihood = -1421.0053  
Iteration 1:   log likelihood = -1386.2926  
Iteration 2:   log likelihood = -1383.8881  
Iteration 3:   log likelihood =  -1383.881  
Iteration 4:   log likelihood =  -1383.881  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(21)       =      74.25
                                                Prob > chi2       =     0.0000
Log likelihood =  -1383.881                     Pseudo R2         =     0.0261

-----------------------------------------------------------------------------------
like_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     1.vet_MetteF |  -.7172447   .4748141    -1.51   0.131    -1.647863    .2133738
                  |
             PS_A |
               1  |  -1.159077   .6818111    -1.70   0.089    -2.495403    .1772478
               2  |  -.1782482   .3927599    -0.45   0.650    -.9480436    .5915471
               3  |  -.0637067    .368552    -0.17   0.863    -.7860554     .658642
               4  |  -.4569616    .382746    -1.19   0.233     -1.20713    .2932067
               5  |  -.6584042   .3597788    -1.83   0.067    -1.363558    .0467492
               6  |  -.3605698   .3622057    -1.00   0.320     -1.07048    .3493403
               7  |  -.6207845   .3696359    -1.68   0.093    -1.345257    .1036886
               8  |    -.35152   .3596378    -0.98   0.328    -1.056397    .3533572
               9  |  -1.050736    .485535    -2.16   0.030    -2.002367   -.0991053
              10  |  -.2472411   .4487476    -0.55   0.582     -1.12677    .6322879
                  |
  vet_MetteF#PS_A |
            1  1  |   1.118255   .9404765     1.19   0.234    -.7250446    2.961556
            1  2  |   .3605698   .5990743     0.60   0.547    -.8135942    1.534734
            1  3  |   .1543611    .559249     0.28   0.783    -.9417469    1.250469
            1  4  |   .7345934   .5758287     1.28   0.202    -.3940101    1.863197
            1  5  |   1.037058   .5348836     1.94   0.053    -.0112946    2.085411
            1  6  |   .6772394   .5439945     1.24   0.213    -.3889701    1.743449
            1  7  |   1.422145    .536059     2.65   0.008      .371489    2.472802
            1  8  |   1.231079   .5362591     2.30   0.022     .1800301    2.282127
            1  9  |   2.203416     .66767     3.30   0.001     .8948068    3.512025
            1 10  |   2.137441   .6298704     3.39   0.001     .9029173    3.371964
                  |
            _cons |  -1.074515   .3095461    -3.47   0.001    -1.681214   -.4678154
-----------------------------------------------------------------------------------

.                 eststo m1a_i

.                 margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1428571   .0440867     3.24   0.001     .0564489    .2292654
          2  |    .137931   .0640329     2.15   0.031     .0124289    .2634332
          3  |   .1666667   .0380363     4.38   0.000     .0921169    .2412164
          4  |    .154321    .028383     5.44   0.000     .0986914    .2099506
          5  |   .1803279   .0348074     5.18   0.000     .1121066    .2485491
          6  |   .1957447   .0258826     7.56   0.000     .1450157    .2464737
          7  |   .1861702   .0283885     6.56   0.000     .1305297    .2418107
          8  |   .2708333   .0286853     9.44   0.000     .2146113    .3270554
          9  |   .2865497   .0345767     8.29   0.000     .2187806    .3543188
         10  |   .3454545   .0641186     5.39   0.000     .2197844    .4711247
         11  |   .5245902    .063941     8.20   0.000     .3992682    .6499122
------------------------------------------------------------------------------

.                 eststo like_vet_ps_a_mf_i

.                 estimates restore m1a_i 
(results m1a_i are active now)

.                 margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2545455    .058737     4.33   0.000      .139423    .3696679
          2  |   .0967742   .0531003     1.82   0.068    -.0073005    .2008489
          3  |   .2222222   .0417834     5.32   0.000     .1403282    .3041162
          4  |   .2426471   .0367593     6.60   0.000     .1706002     .314694
          5  |   .1777778   .0329053     5.40   0.000     .1132845    .2422711
          6  |   .1502146   .0234063     6.42   0.000     .1043391    .1960901
          7  |   .1923077   .0292136     6.58   0.000       .13505    .2495654
          8  |   .1550802   .0264707     5.86   0.000     .1031986    .2069618
          9  |   .1937173   .0285964     6.77   0.000     .1376694    .2497652
         10  |   .1066667   .0356443     2.99   0.003     .0368051    .1765283
         11  |   .2105263   .0539989     3.90   0.000     .1046905    .3163621
------------------------------------------------------------------------------

.                 eststo like_vet_ps_a_ll_i

.                 coefplot        (like_vet_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")
> ) /// ) ///
>                                         (like_vet_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) 
> ///)
>                                         , vert ytitle("Probability of" "Liking the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                         ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(like_vet_ps_A_i, replace) fxsize(100)

.                 logit like_vet_both_yes i.vet_MetteF##i.PS_V  

Iteration 0:   log likelihood = -1423.4842  
Iteration 1:   log likelihood = -1383.4121  
Iteration 2:   log likelihood = -1381.3816  
Iteration 3:   log likelihood = -1381.3756  
Iteration 4:   log likelihood = -1381.3756  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(21)       =      84.22
                                                Prob > chi2       =     0.0000
Log likelihood = -1381.3756                     Pseudo R2         =     0.0296

-----------------------------------------------------------------------------------
like_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     1.vet_MetteF |   .9721869   .2795981     3.48   0.001     .4241847    1.520189
                  |
             PS_V |
               1  |  -.1855964   .3739475    -0.50   0.620      -.91852    .5473271
               2  |   .1612567   .3164345     0.51   0.610    -.4589434    .7814569
               3  |   .5404045   .3105477     1.74   0.082    -.0682578    1.149067
               4  |    .347702   .3363775     1.03   0.301    -.3115859     1.00699
               5  |   1.051101   .2845315     3.69   0.000     .4934297    1.608773
               6  |   .7468236   .3327139     2.24   0.025     .0947165    1.398931
               7  |   1.369353    .300103     4.56   0.000     .7811621    1.957544
               8  |   1.059198   .3263042     3.25   0.001     .4196539    1.698743
               9  |   1.575889   .3613125     4.36   0.000     .8677296    2.284049
              10  |   2.040028   .4081163     5.00   0.000     1.240134    2.839921
                  |
  vet_MetteF#PS_V |
            1  1  |   .0692669   .4548848     0.15   0.879     -.822291    .9608248
            1  2  |  -.0892832   .3999906    -0.22   0.823    -.8732503    .6946839
            1  3  |  -.5970359   .3969183    -1.50   0.133    -1.374981    .1809097
            1  4  |   -.394222   .4346867    -0.91   0.364    -1.246192    .4577482
            1  5  |  -1.003936    .377932    -2.66   0.008    -1.744669   -.2632025
            1  6  |  -.9170351   .4366686    -2.10   0.036     -1.77289   -.0611803
            1  7  |  -1.559553   .4146402    -3.76   0.000    -2.372233   -.7468727
            1  8  |  -1.154508   .4472527    -2.58   0.010    -2.031108   -.2779093
            1  9  |  -2.043188   .5409308    -3.78   0.000    -3.103393   -.9829835
            1 10  |   -1.36523   .5740766    -2.38   0.017    -2.490399     -.24006
                  |
            _cons |  -2.157811   .2202348    -9.80   0.000    -2.589463   -1.726158
-----------------------------------------------------------------------------------

.                 eststo m1b_i

.                 margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2340426   .0308795     7.58   0.000     .1735198    .2945653
          2  |   .2138365   .0325161     6.58   0.000      .150106    .2775669
          3  |    .247191   .0323332     7.65   0.000     .1838191    .3105629
          4  |   .2240437   .0308219     7.27   0.000     .1636339    .2844535
          5  |   .2258065   .0375476     6.01   0.000     .1522145    .2993984
          6  |   .2426036   .0329736     7.36   0.000     .1779764    .3072307
          7  |    .204918    .036544     5.61   0.000     .1332931     .276543
          8  |   .2016807    .036783     5.48   0.000     .1295873     .273774
          9  |   .2173913   .0430031     5.06   0.000     .1331068    .3016758
         10  |   .1607143   .0490781     3.27   0.001     .0645229    .2569057
         11  |       .375   .0855816     4.38   0.000      .207263     .542737
------------------------------------------------------------------------------

.                 eststo like_vet_ps_v_mf_i

.                 estimates restore m1b_i
(results m1b_i are active now)

.                 margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1036036   .0204532     5.07   0.000     .0635161    .1436911
          2  |   .0875912   .0241526     3.63   0.000     .0402529    .1349296
          3  |   .1195652    .023919     5.00   0.000     .0726849    .1664455
          4  |   .1655629   .0302475     5.47   0.000     .1062789     .224847
          5  |    .140625   .0307268     4.58   0.000     .0804015    .2008485
          6  |   .2484848   .0336416     7.39   0.000     .1825485    .3144212
          7  |   .1960784   .0393117     4.99   0.000     .1190289    .2731279
          8  |      .3125   .0437978     7.14   0.000     .2266579    .3983421
          9  |        .25   .0451447     5.54   0.000      .161518     .338482
         10  |   .3584906   .0658722     5.44   0.000     .2293833    .4875978
         11  |   .4705882   .0856008     5.50   0.000     .3028137    .6383627
------------------------------------------------------------------------------

.                 eststo like_vet_ps_v_ll_i

.                 coefplot        (like_vet_ps_v_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")
> ) /// ) ///
>                                         (like_vet_ps_v_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) 
> ///)
>                                         , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                         ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(like_vet_ps_V_i, replace) fxsize(90)

.                 *Immigrants
.                 logit like_immi_both_yes i.immigrants_MetteF##i.PS_A  

note: 1.immigrants_MetteF#1.PS_A != 0 predicts failure perfectly
      1.immigrants_MetteF#1.PS_A dropped and 33 obs not used

Iteration 0:   log likelihood =  -1054.788  
Iteration 1:   log likelihood = -1021.2613  
Iteration 2:   log likelihood = -1009.0786  
Iteration 3:   log likelihood = -1007.9856  
Iteration 4:   log likelihood = -1007.9839  
Iteration 5:   log likelihood = -1007.9839  

Logistic regression                             Number of obs     =      2,770
                                                LR chi2(20)       =      93.61
                                                Prob > chi2       =     0.0000
Log likelihood = -1007.9839                     Pseudo R2         =     0.0444

----------------------------------------------------------------------------------------
    like_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
   1.immigrants_MetteF |  -.5061271   .4931172    -1.03   0.305    -1.472619    .4603649
                       |
                  PS_A |
                    1  |  -.7731899   .6875728    -1.12   0.261    -2.120808    .5744281
                    2  |  -.7856124   .4583689    -1.71   0.087    -1.683999    .1127742
                    3  |   -.394536   .3862268    -1.02   0.307    -1.151527    .3624547
                    4  |  -1.161848   .4541266    -2.56   0.011     -2.05192   -.2717761
                    5  |  -1.096179   .3902324    -2.81   0.005     -1.86102   -.3313374
                    6  |  -.7326046   .3861711    -1.90   0.058    -1.489486    .0242768
                    7  |  -.8000011   .3827867    -2.09   0.037    -1.550249    -.049753
                    8  |   -1.49103   .4515019    -3.30   0.001    -2.375957   -.6061022
                    9  |  -1.178655   .5607344    -2.10   0.036    -2.277674   -.0796358
                   10  |  -.9673459   .5052914    -1.91   0.056    -1.957699    .0230071
                       |
immigrants_MetteF#PS_A |
                 1  1  |          0  (empty)
                 1  2  |    .652081   .6658996     0.98   0.327    -.6530583     1.95722
                 1  3  |  -.0593027   .6110772    -0.10   0.923    -1.256992    1.138387
                 1  4  |   .6014373   .6715823     0.90   0.370    -.7148399    1.917714
                 1  5  |    .906077   .5828606     1.55   0.120    -.2363088    2.048463
                 1  6  |   .4142485   .5944896     0.70   0.486    -.7509296    1.579427
                 1  7  |   .7932215   .5750414     1.38   0.168    -.3338389    1.920282
                 1  8  |   2.057471   .6163773     3.34   0.001     .8493941    3.265549
                 1  9  |   2.251367   .7281329     3.09   0.002     .8242522    3.678481
                 1 10  |   2.733205   .7027099     3.89   0.000     1.355919    4.110491
                       |
                 _cons |  -1.306252   .3126602    -4.18   0.000    -1.919054   -.6934489
----------------------------------------------------------------------------------------

.                 eststo m2a_i

.                 margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,770
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1403509   .0460077     3.05   0.002     .0501774    .2305244
          2  |          .  (not estimable)
          3  |       .125   .0324297     3.85   0.000      .061439     .188561
          4  |   .0939597   .0239029     3.93   0.000     .0471108    .1408087
          5  |   .0852713   .0245897     3.47   0.001     .0370764    .1334662
          6  |   .1189427   .0214861     5.54   0.000     .0768307    .1610548
          7  |   .1061453   .0230228     4.61   0.000     .0610215     .151269
          8  |   .1395349   .0236314     5.90   0.000     .0932183    .1858515
          9  |   .2234043   .0303784     7.35   0.000     .1638638    .2829447
         10  |   .3230769   .0580051     5.57   0.000     .2093891    .4367648
         11  |   .4883721   .0762287     6.41   0.000     .3389667    .6377775
------------------------------------------------------------------------------

.                 eststo like_immi_ps_a_mf_i

.                 estimates restore m2a_i 
(results m2a_i are active now)

.                 margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,770
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2131148   .0524321     4.06   0.000     .1103497    .3158799
          2  |   .1111111   .0604812     1.84   0.066    -.0074299    .2296521
          3  |   .1098901   .0327854     3.35   0.001     .0456319    .1741483
          4  |   .1543624   .0295985     5.22   0.000     .0963504    .2123744
          5  |    .078125   .0237206     3.29   0.001     .0316334    .1246166
          6  |   .0829876   .0177699     4.67   0.000     .0481591     .117816
          7  |   .1151832   .0230996     4.99   0.000     .0699088    .1604576
          8  |   .1084906   .0213595     5.08   0.000     .0666267    .1503544
          9  |   .0574713    .017644     3.26   0.001     .0228896    .0920529
         10  |   .0769231   .0330515     2.33   0.020     .0121434    .1417027
         11  |   .0933333   .0335901     2.78   0.005     .0274979    .1591688
------------------------------------------------------------------------------

.                 eststo like_immi_ps_a_ll_i

.                 coefplot        (like_immi_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}"
> )) /// ) ///
>                                         (like_immi_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}"))
>  ///)
>                                         , vert ytitle("Probability of" "Liking the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                         ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(like_immi_ps_A_i, replace) fxsize(100)             

.                 logit like_immi_both_yes i.immigrants_MetteF##i.PS_V   

Iteration 0:   log likelihood = -1059.1103  
Iteration 1:   log likelihood = -1007.4085  
Iteration 2:   log likelihood = -1000.1518  
Iteration 3:   log likelihood = -1000.0833  
Iteration 4:   log likelihood = -1000.0832  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(21)       =     118.05
                                                Prob > chi2       =     0.0000
Log likelihood = -1000.0832                     Pseudo R2         =     0.0557

----------------------------------------------------------------------------------------
    like_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
   1.immigrants_MetteF |   1.668263   .4615846     3.61   0.000      .763574    2.572952
                       |
                  PS_V |
                    1  |   .2943561    .587403     0.50   0.616    -.8569326    1.445645
                    2  |   .6733293   .5268269     1.28   0.201    -.3592324    1.705891
                    3  |   .3835486   .5664015     0.68   0.498    -.7265779    1.493675
                    4  |   .9415933   .5397428     1.74   0.081    -.1162832     1.99947
                    5  |   1.447035   .4806321     3.01   0.003     .5050138    2.389057
                    6  |    1.63474   .5040653     3.24   0.001     .6467907     2.62269
                    7  |   2.282767   .4726827     4.83   0.000     1.356326    3.209208
                    8  |      2.247   .4824902     4.66   0.000     1.301337    3.192663
                    9  |    2.45049   .5058689     4.84   0.000     1.459005    3.441975
                   10  |   2.855955   .5463294     5.23   0.000     1.785169    3.926741
                       |
immigrants_MetteF#PS_V |
                 1  1  |  -.6612063   .6824521    -0.97   0.333    -1.998788    .6763753
                 1  2  |  -.5397979   .6009579    -0.90   0.369    -1.717654     .638058
                 1  3  |  -.1547071   .6364463    -0.24   0.808    -1.402119    1.092705
                 1  4  |  -1.182755   .6427095    -1.84   0.066    -2.442443    .0769322
                 1  5  |  -1.080043   .5586312    -1.93   0.053     -2.17494    .0148535
                 1  6  |  -1.811364   .6135391    -2.95   0.003    -3.013879   -.6088494
                 1  7  |  -2.005135   .5678416    -3.53   0.000    -3.118085   -.8921864
                 1  8  |  -2.267619    .608698    -3.73   0.000    -3.460645   -1.074593
                 1  9  |  -1.920543   .6514035    -2.95   0.003    -3.197271    -.643816
                 1 10  |  -2.249819   .7241517    -3.11   0.002    -3.669131    -.830508
                       |
                 _cons |  -3.506543   .4143238    -8.46   0.000    -4.318602   -2.694483
----------------------------------------------------------------------------------------

.                 eststo m2b_i

.                 margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1372549    .024093     5.70   0.000     .0900336    .1844762
          2  |   .0992908   .0251847     3.94   0.000     .0499296    .1486519
          3  |   .1538462   .0267444     5.75   0.000     .1014282    .2062641
          4  |   .1666667   .0287527     5.80   0.000     .1103123     .223021
          5  |   .1111111   .0279974     3.97   0.000     .0562373    .1659849
          6  |    .186747   .0302472     6.17   0.000     .1274635    .2460305
          7  |   .1176471   .0295351     3.98   0.000     .0597594    .1755348
          8  |   .1735537   .0344296     5.04   0.000      .106073    .2410344
          9  |   .1348315   .0362035     3.72   0.000     .0638738    .2057891
         10  |    .212766   .0596972     3.56   0.000     .0957616    .3297704
         11  |   .2258065   .0750952     3.01   0.003     .0786226    .3729903
------------------------------------------------------------------------------

.                 eststo like_immi_ps_a_mf_i

.                 estimates restore m2b_i 
(results m2b_i are active now)

.                 margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0291266   .0117164     2.49   0.013      .006163    .0520903
          2  |   .0387097   .0154943     2.50   0.012     .0083415    .0690779
          3  |   .0555556   .0170732     3.25   0.001     .0220926    .0890185
          4  |   .0421687   .0155986     2.70   0.007      .011596    .0727414
          5  |   .0714286   .0229434     3.11   0.002     .0264603    .1163969
          6  |   .1130952   .0244346     4.63   0.000     .0652042    .1609863
          7  |   .1333333   .0331742     4.02   0.000     .0683131    .1983536
          8  |   .2272727   .0399568     5.69   0.000     .1489589    .3055866
          9  |   .2210526   .0425736     5.19   0.000       .13761    .3044953
         10  |   .2580645   .0555714     4.64   0.000     .1491466    .3669825
         11  |   .3428572   .0802329     4.27   0.000     .1856036    .5001107
------------------------------------------------------------------------------

.                 eststo like_immi_ps_a_ll_I

.                 coefplot        (like_immi_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}"
> )) /// ) ///
>                                         (like_immi_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}"))
>  ///)
>                                         , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                         ylabel(0(.1).5,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(like_immi_ps_V_i, replace) fxsize(90)              

.                 graph close

.                 *FIGURE C1: Liking as a function of Party Sympathy
.                 grc1leg2  like_vet_ps_A_i like_vet_ps_V_i like_immi_ps_A_i like_immi_ps_V_i , ycommon pos(12) title("") note("Note: Estimates with 95% confidence intervals (n=2,802/2,803)")

.                 graph export FIGURE_C1.png, replace     
(file FIGURE_C1.png written in PNG format)

. 
.                 
.                 ***Commenting***
.                 *Vets
.                 logit comment_vet_both_yes i.vet_MetteF##i.PS_A  

note: 1.PS_A != 0 predicts failure perfectly
      1.PS_A dropped and 60 obs not used

note: 0.vet_MetteF#4.PS_A != 0 predicts failure perfectly
      0.vet_MetteF#4.PS_A dropped and 135 obs not used

note: 1.vet_MetteF#1.PS_A omitted because of collinearity
note: 1.vet_MetteF#4.PS_A omitted because of collinearity
Iteration 0:   log likelihood = -340.07491  
Iteration 1:   log likelihood = -330.25933  
Iteration 2:   log likelihood = -328.14607  
Iteration 3:   log likelihood = -328.06789  
Iteration 4:   log likelihood = -328.06768  
Iteration 5:   log likelihood = -328.06768  

Logistic regression                             Number of obs     =      2,608
                                                LR chi2(18)       =      24.01
                                                Prob > chi2       =     0.1546
Log likelihood = -328.06768                     Pseudo R2         =     0.0353

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |  -1.581603   1.133903    -1.39   0.163    -3.804013    .6408067
                     |
                PS_A |
                  1  |          0  (empty)
                  2  |  -1.336033   .8831292    -1.51   0.130    -3.066934    .3948688
                  3  |  -.7202281   .6908266    -1.04   0.297    -2.074223    .6337672
                  4  |   .7427441   1.128983     0.66   0.511    -1.470023    2.955511
                  5  |  -.9290936     .64567    -1.44   0.150    -2.194584    .3363964
                  6  |  -1.954278   .8804336    -2.22   0.026    -3.679897   -.2286602
                  7  |  -1.049038   .6892767    -1.52   0.128    -2.399995      .30192
                  8  |  -.4612508   .6214607    -0.74   0.458    -1.679292    .7567898
                  9  |  -1.051781    .885046    -1.19   0.235    -2.786439    .6828774
                 10  |   -1.47982   1.134665    -1.30   0.192    -3.703723    .7440824
                     |
     vet_MetteF#PS_A |
               0  1  |          0  (empty)
               0  4  |          0  (empty)
               1  1  |          0  (empty)
               1  2  |   1.613019   1.518777     1.06   0.288    -1.363729    4.589768
               1  3  |   1.171062   1.322762     0.89   0.376    -1.421504    3.763628
               1  4  |          0  (omitted)
               1  5  |   1.414265   1.266512     1.12   0.264    -1.068052    3.896583
               1  6  |   2.481365   1.413066     1.76   0.079    -.2881949    5.250924
               1  7  |   .3970484   1.412598     0.28   0.779    -2.371593     3.16569
               1  8  |   1.274199   1.255018     1.02   0.310    -1.185591    3.733989
               1  9  |   1.189931   1.678676     0.71   0.478    -2.100214    4.480076
               1 10  |   3.563881   1.570022     2.27   0.023      .486694    6.641068
                     |
               _cons |  -2.545531   .5192378    -4.90   0.000    -3.563219   -1.527844
--------------------------------------------------------------------------------------

.                 eststo m3a_i

.                 margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,608
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .015873   .0157465     1.01   0.313    -.0149896    .0467357
          2  |          .  (not estimable)
          3  |   .0208333   .0145771     1.43   0.153    -.0077373     .049404
          4  |   .0246914   .0121923     2.03   0.043     .0007949    .0485878
          5  |   .0327869   .0161225     2.03   0.042     .0011874    .0643863
          6  |   .0255319   .0102894     2.48   0.013      .005365    .0456988
          7  |   .0265957   .0117347     2.27   0.023     .0035961    .0495954
          8  |   .0083333    .005868     1.42   0.156    -.0031676    .0198343
          9  |   .0350877    .014071     2.49   0.013     .0075092    .0626663
         10  |   .0181818   .0180158     1.01   0.313    -.0171284    .0534921
         11  |   .1147541   .0408085     2.81   0.005     .0347708    .1947374
------------------------------------------------------------------------------

.                 eststo comment_vet_ps_a_mf_i

.                 estimates restore m3a_i 
(results m3a_i are active now)

.                 margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,608
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0727273   .0350164     2.08   0.038     .0040965    .1413581
          2  |          .  (not estimable)
          3  |    .020202     .01414     1.43   0.153    -.0075118    .0479158
          4  |   .0367647   .0161366     2.28   0.023     .0051375    .0683919
          5  |          .  (not estimable)
          6  |   .0300429   .0111833     2.69   0.007     .0081241    .0519618
          7  |    .010989   .0077276     1.42   0.155    -.0041568    .0261348
          8  |    .026738   .0117966     2.27   0.023      .003617     .049859
          9  |   .0471204   .0153323     3.07   0.002     .0170697    .0771711
         10  |   .0266667   .0186031     1.43   0.152    -.0097947     .063128
         11  |   .0175439   .0173893     1.01   0.313    -.0165385    .0516262
------------------------------------------------------------------------------

.                 eststo comment_vet_ps_a_ll_i

.                 coefplot        (comment_vet_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats
> }")) /// ) ///
>                                         (comment_vet_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}"
> )) ///)
>                                         , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                         ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(comment_vet_ps_A_i, replace) fxsize(100)

.                 logit comment_vet_both_yes i.vet_MetteF##i.PS_V

Iteration 0:   log likelihood = -345.53222  
Iteration 1:   log likelihood = -339.77723  
Iteration 2:   log likelihood = -338.80161  
Iteration 3:   log likelihood = -338.79734  
Iteration 4:   log likelihood = -338.79734  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(21)       =      13.47
                                                Prob > chi2       =     0.8913
Log likelihood = -338.79734                     Pseudo R2         =     0.0195

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |  -.0165293   .6138084    -0.03   0.979    -1.219572    1.186513
                     |
                PS_V |
                  1  |  -.2157086   .7156063    -0.30   0.763    -1.618271    1.186854
                  2  |    .005571   .6139079     0.01   0.993    -1.197666    1.208808
                  3  |  -.0206193   .6542928    -0.03   0.975     -1.30301    1.261771
                  4  |  -1.260666   1.085895    -1.16   0.246    -3.388982    .8676493
                  5  |   .3063742   .5867302     0.52   0.602    -.8435958    1.456344
                  6  |  -1.031602   1.086829    -0.95   0.343    -3.161747    1.098544
                  7  |    .520128    .616962     0.84   0.399    -.6890953    1.729351
                  8  |  -.2231436   .8260795    -0.27   0.787     -1.84223    1.395943
                  9  |   .7701082   .7243132     1.06   0.288    -.6495196    2.189736
                 10  |   .0870114   1.096175     0.08   0.937    -2.061452    2.235475
                     |
     vet_MetteF#PS_V |
               1  1  |  -.1354869   1.028251    -0.13   0.895    -2.150822    1.879848
               1  2  |   .0506235   .8877656     0.06   0.955    -1.689365    1.790612
               1  3  |   -.473677     .98613    -0.48   0.631    -2.406456    1.459102
               1  4  |   .7498408   1.375802     0.55   0.586    -1.946681    3.446362
               1  5  |   .2917112   .8251836     0.35   0.724    -1.325619    1.909041
               1  6  |    1.24726   1.282629     0.97   0.331    -1.266646    3.761166
               1  7  |  -.9891065   1.046257    -0.95   0.344    -3.039732    1.061519
               1  8  |   .0165293   1.182786     0.01   0.989    -2.301689    2.334747
               1  9  |  -.4658968   1.117413    -0.42   0.677    -2.655987    1.724193
               1 10  |   .8049868   1.392981     0.58   0.563    -1.925207     3.53518
                     |
               _cons |  -3.583519   .4138796    -8.66   0.000    -4.394708    -2.77233
--------------------------------------------------------------------------------------

.                 eststo m3b_i

.                 margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0265957   .0117347     2.27   0.023     .0035961    .0495954
          2  |   .0188679   .0107901     1.75   0.080    -.0022804    .0400162
          3  |   .0280899   .0123845     2.27   0.023     .0038167     .052363
          4  |   .0163934   .0093869     1.75   0.081    -.0020045    .0347913
          5  |    .016129   .0113126     1.43   0.154    -.0060433    .0383013
          6  |   .0473373   .0163353     2.90   0.004     .0153206    .0793539
          7  |   .0327869   .0161225     2.03   0.042     .0011874    .0643863
          8  |   .0168067   .0117839     1.43   0.154    -.0062892    .0399027
          9  |   .0217391   .0152039     1.43   0.153    -.0080599    .0515382
         10  |   .0357143   .0247988     1.44   0.150    -.0128904    .0843189
         11  |      .0625   .0427908     1.46   0.144    -.0213685    .1463685
------------------------------------------------------------------------------

.                 eststo comment_vet_ps_v_mf_i

.                 estimates restore m3b_i
(results m3b_i are active now)

.                 margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .027027   .0108836     2.48   0.013     .0056955    .0483585
          2  |   .0218978   .0125035     1.75   0.080    -.0026086    .0464043
          3  |   .0271739   .0119863     2.27   0.023     .0036812    .0506666
          4  |   .0264901   .0130684     2.03   0.043     .0008764    .0521037
          5  |   .0078125   .0077819     1.00   0.315    -.0074398    .0230648
          6  |   .0363636    .014573     2.50   0.013     .0078011    .0649261
          7  |   .0098039   .0097557     1.00   0.315     -.009317    .0289248
          8  |   .0446429   .0195142     2.29   0.022     .0063958    .0828899
          9  |   .0217391   .0152039     1.43   0.153    -.0080599    .0515382
         10  |   .0566038   .0317418     1.78   0.075    -.0056091    .1188166
         11  |   .0294118    .028976     1.02   0.310    -.0273802    .0862037
------------------------------------------------------------------------------

.                 eststo comment_vet_ps_v_ll_i

.                 coefplot        (comment_vet_ps_v_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats
> }")) /// ) ///
>                                         (comment_vet_ps_v_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}"
> )) ///)
>                                         , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                         ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(comment_vet_ps_V_i, replace) fxsize(90)

.                 *Immigrants
.                 logit comment_immi_both_yes i.immigrants_MetteF##i.PS_A

Iteration 0:   log likelihood =  -418.2503  
Iteration 1:   log likelihood = -408.82576  
Iteration 2:   log likelihood = -407.55134  
Iteration 3:   log likelihood = -407.54102  
Iteration 4:   log likelihood = -407.54098  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(21)       =      21.42
                                                Prob > chi2       =     0.4336
Log likelihood = -407.54098                     Pseudo R2         =     0.0256

----------------------------------------------------------------------------------------
 comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
   1.immigrants_MetteF |  -.3523553   .9320711    -0.38   0.705    -2.179181     1.47447
                       |
                  PS_A |
                    1  |  -.2962658   1.178574    -0.25   0.802    -2.606228    2.013696
                    2  |   .3109389    .727328     0.43   0.669    -1.114598    1.736476
                    3  |  -.3985447   .7466721    -0.53   0.594    -1.861995    1.064906
                    4  |  -.4721565   .7801533    -0.61   0.545    -2.001229    1.056916
                    5  |   .0130154   .6620233     0.02   0.984    -1.284526    1.310557
                    6  |  -.1682139   .6935699    -0.24   0.808    -1.527586    1.191158
                    7  |   -.574286   .7225619    -0.79   0.427    -1.990481    .8419094
                    8  |  -.0707155   .6939732    -0.10   0.919    -1.430878    1.289447
                    9  |  -1.197052   1.168845    -1.02   0.306    -3.487947    1.093843
                   10  |   .3227734    .751572     0.43   0.668    -1.150281    1.795827
                       |
immigrants_MetteF#PS_A |
                 1  1  |   .1447159   1.714196     0.08   0.933    -3.215047    3.504478
                 1  2  |  -.9285786   1.247795    -0.74   0.457    -3.374211    1.517054
                 1  3  |   .1222913   1.154382     0.11   0.916    -2.140256    2.384838
                 1  4  |  -1.065652   1.461027    -0.73   0.466    -3.929212    1.797907
                 1  5  |  -1.011863   1.137653    -0.89   0.374    -3.241622    1.217896
                 1  6  |  -.5894718   1.156822    -0.51   0.610      -2.8568    1.677857
                 1  7  |   .4968441   1.089928     0.46   0.648    -1.639375    2.633063
                 1  8  |   .3947403   1.056635     0.37   0.709    -1.676227    2.465708
                 1  9  |   1.061251   1.549275     0.68   0.493    -1.975273    4.097775
                 1 10  |   .4011454   1.200573     0.33   0.738    -1.951934    2.754225
                       |
                 _cons |  -2.961831   .5920935    -5.00   0.000    -4.122313   -1.801349
----------------------------------------------------------------------------------------

.                 eststo m4a_i

.                 margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0350877   .0243716     1.44   0.150    -.0126797    .0828552
          2  |    .030303   .0298404     1.02   0.310     -.028183    .0887891
          3  |   .0192308   .0134668     1.43   0.153    -.0071637    .0456253
          4  |   .0268456   .0132414     2.03   0.043     .0008929    .0527983
          5  |   .0077522    .007722     1.00   0.315    -.0073826     .022887
          6  |   .0132159   .0075796     1.74   0.081    -.0016399    .0280716
          7  |   .0167598   .0095948     1.75   0.081    -.0020458    .0355653
          8  |   .0325581   .0121038     2.69   0.007     .0088351    .0562812
          9  |   .0478723   .0155708     3.07   0.002     .0173541    .0783906
         10  |   .0307692   .0214198     1.44   0.151    -.0112128    .0727513
         11  |   .0697674   .0388497     1.80   0.073    -.0063766    .1459115
------------------------------------------------------------------------------

.                 eststo comment_immi_ps_a_mf_i

.                 estimates restore m4a_i 
(results m4a_i are active now)

.                 margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0491803   .0276873     1.78   0.076    -.0050857    .1034463
          2  |    .037037   .0363447     1.02   0.308    -.0341973    .1082713
          3  |   .0659341    .026015     2.53   0.011     .0149457    .1169224
          4  |    .033557   .0147532     2.27   0.023     .0046413    .0624728
          5  |     .03125   .0153789     2.03   0.042     .0011079    .0613921
          6  |   .0497925   .0140114     3.55   0.000     .0223306    .0772545
          7  |   .0418848   .0144951     2.89   0.004      .013475    .0702946
          8  |   .0283019   .0113895     2.48   0.013     .0059788    .0506249
          9  |    .045977   .0158772     2.90   0.004     .0148582    .0770958
         10  |   .0153846   .0152658     1.01   0.314    -.0145358    .0453051
         11  |   .0666667   .0288033     2.31   0.021     .0102133    .1231201
------------------------------------------------------------------------------

.                 eststo comment_immi_ps_a_ll_i

.                 coefplot        (comment_immi_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrat
> s}")) /// ) ///
>                                         (comment_immi_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}
> ")) ///)
>                                         , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                         ylabel(0(.05).251,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(comment_immi_ps_A_i, replace) fxsize(100)          

.                 logit comment_immi_both_yes i.immigrants_MetteF##i.PS_V   

note: 0.immigrants_MetteF#4.PS_V != 0 predicts failure perfectly
      0.immigrants_MetteF#4.PS_V dropped and 126 obs not used

note: 1.immigrants_MetteF#4.PS_V omitted because of collinearity
Iteration 0:   log likelihood = -413.71916  
Iteration 1:   log likelihood = -402.35917  
Iteration 2:   log likelihood = -400.01123  
Iteration 3:   log likelihood = -399.98737  
Iteration 4:   log likelihood = -399.98733  
Iteration 5:   log likelihood = -399.98733  

Logistic regression                             Number of obs     =      2,676
                                                LR chi2(20)       =      27.46
                                                Prob > chi2       =     0.1227
Log likelihood = -399.98733                     Pseudo R2         =     0.0332

----------------------------------------------------------------------------------------
 comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
   1.immigrants_MetteF |  -.4621893   .4939338    -0.94   0.349    -1.430282    .5059031
                       |
                  PS_V |
                    1  |  -.0358868   .4773259    -0.08   0.940    -.9714283    .8996548
                    2  |   .1431008   .4391623     0.33   0.745    -.7176415    1.003843
                    3  |   .0160831   .4620799     0.03   0.972    -.8895769     .921743
                    4  |  -.7898408   .8099369    -0.98   0.329    -2.377288    .7976064
                    5  |  -.2603899   .4950814    -0.53   0.599    -1.230732    .7099518
                    6  |  -.6512562   .6627025    -0.98   0.326    -1.950129    .6476167
                    7  |  -1.816244    1.05129    -1.73   0.084    -3.876735    .2442478
                    8  |   -.964348   .7789673    -1.24   0.216    -2.491096    .5623997
                    9  |   .4414909   .5599832     0.79   0.430    -.6560559    1.539038
                   10  |   .0717439   .7914167     0.09   0.928    -1.479404    1.622892
                       |
immigrants_MetteF#PS_V |
                 0  4  |          0  (empty)
                 1  1  |  -.8681464   .9396635    -0.92   0.356    -2.709853    .9735603
                 1  2  |  -.6012965    .772279    -0.78   0.436    -2.114935    .9123425
                 1  3  |  -.3923615   .7858427    -0.50   0.618    -1.932585    1.147862
                 1  4  |          0  (omitted)
                 1  5  |   .4746895   .7363324     0.64   0.519    -.9684955    1.917874
                 1  6  |  -.7821348   1.263164    -0.62   0.536     -3.25789     1.69362
                 1  7  |   1.777658   1.229509     1.45   0.148    -.6321354    4.187451
                 1  8  |   .5288807   1.125263     0.47   0.638    -1.676595    2.734356
                 1  9  |  -.9328387   1.217889    -0.77   0.444    -3.319857     1.45418
                 1 10  |   1.031957   1.069262     0.97   0.334    -1.063758    3.127672
                       |
                 _cons |  -2.875104   .3098988    -9.28   0.000    -3.482495   -2.267714
----------------------------------------------------------------------------------------

.                 eststo m4b_i

.                 margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,676
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0343137   .0127449     2.69   0.007     .0093342    .0592933
          2  |   .0141844   .0099585     1.42   0.154    -.0053339    .0337027
          3  |    .021978   .0108676     2.02   0.043      .000678    .0432781
          4  |   .0238095   .0117622     2.02   0.043     .0007561     .046863
          5  |    .015873   .0111345     1.43   0.154    -.0059502    .0376962
          6  |   .0421687   .0155986     2.70   0.007      .011596    .0727414
          7  |   .0084034    .008368     1.00   0.315    -.0079976    .0248043
          8  |   .0330579   .0162534     2.03   0.042     .0012017     .064914
          9  |   .0224719   .0157105     1.43   0.153    -.0083201    .0532639
         10  |   .0212766    .021049     1.01   0.312    -.0199787    .0625319
         11  |   .0967742   .0531003     1.82   0.068    -.0073005    .2008489
------------------------------------------------------------------------------

.                 eststo comment_immi_ps_a_mf_i

.                 estimates restore m4b_i 
(results m4b_i are active now)

.                 margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,676
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0533981   .0156644     3.41   0.001     .0226965    .0840997
          2  |   .0516129   .0177708     2.90   0.004     .0167828     .086443
          3  |   .0611111   .0178538     3.42   0.001     .0261183    .0961039
          4  |   .0542169   .0175756     3.08   0.002     .0197694    .0886643
          5  |          .  (not estimable)
          6  |   .0416667   .0154169     2.70   0.007       .01145    .0718833
          7  |   .0285714   .0162584     1.76   0.079    -.0032944    .0604372
          8  |   .0090909   .0090495     1.00   0.315    -.0086458    .0268276
          9  |   .0210526   .0147289     1.43   0.153    -.0078155    .0499208
         10  |   .0806452   .0345808     2.33   0.020     .0128681    .1484223
         11  |   .0571429   .0392347     1.46   0.145    -.0197557    .1340414
------------------------------------------------------------------------------

.                 eststo comment_immi_ps_a_ll_I

.                 coefplot        (comment_immi_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrat
> s}")) /// ) ///
>                                         (comment_immi_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}
> ")) ///)
>                                         , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                         ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(comment_immi_ps_V_i, replace)      fxsize(90)              

.                 graph close

.                 *FIGURE C2: Commenting as a function of Party Sympathy
.                 grc1leg2  comment_vet_ps_A_i comment_vet_ps_V_i comment_immi_ps_A_i comment_immi_ps_V_i , ycommon pos(12) title("") note("Note: Estimates with 95% confidence intervals (n=2,802/2,803)")

.                 graph export FIGURE_C2.png, replace
(file FIGURE_C2.png written in PNG format)

.                 
.                 
.         ***Sharing***
.                 *Vets
.                 logit share_vet_both_yes i.vet_MetteF##i.PS_A 

Iteration 0:   log likelihood = -476.70586  
Iteration 1:   log likelihood = -453.23859  
Iteration 2:   log likelihood = -448.93986  
Iteration 3:   log likelihood = -448.87649  
Iteration 4:   log likelihood = -448.87641  
Iteration 5:   log likelihood = -448.87641  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(21)       =      55.66
                                                Prob > chi2       =     0.0001
Log likelihood = -448.87641                     Pseudo R2         =     0.0584

------------------------------------------------------------------------------------
share_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
      1.vet_MetteF |  -.8956714   .7328539    -1.22   0.222    -2.332039    .5406958
                   |
              PS_A |
                1  |  -1.301137   1.104721    -1.18   0.239     -3.46635    .8640765
                2  |  -1.781503   .8350953    -2.13   0.033     -3.41826   -.1447463
                3  |  -1.165699   .6282582    -1.86   0.064    -2.397062    .0656649
                4  |  -1.388842   .6668646    -2.08   0.037    -2.695873   -.0818115
                5  |  -1.947367   .6644107    -2.93   0.003    -3.249588   -.6451457
                6  |  -1.988713    .725255    -2.74   0.006    -3.410186   -.5672391
                7  |  -1.723131   .6652363    -2.59   0.010     -3.02697   -.4192918
                8  |  -1.168965   .5791086    -2.02   0.044    -2.303997   -.0339328
                9  |  -1.497251   .8371221    -1.79   0.074    -3.137981    .1434778
               10  |  -.4839367   .6752353    -0.72   0.474    -1.807374    .8395002
                   |
   vet_MetteF#PS_A |
             1  1  |   .9646643   1.614349     0.60   0.550    -2.199401     4.12873
             1  2  |   .9270876   1.248208     0.74   0.458    -1.519354     3.37353
             1  3  |   .9033343   .9580111     0.94   0.346     -.974333    2.781002
             1  4  |   1.231838   1.001626     1.23   0.219    -.7313133     3.19499
             1  5  |   2.105371   .9342747     2.25   0.024     .2742263    3.936516
             1  6  |   .8627014   1.102149     0.78   0.434    -1.297472    3.022875
             1  7  |   1.583369   .9470413     1.67   0.095    -.2727977    3.439536
             1  8  |   1.274325   .8959078     1.42   0.155    -.4816216    3.030272
             1  9  |   2.567693   1.102027     2.33   0.020     .4077592    4.727626
             1 10  |   2.173417   .9506309     2.29   0.022      .310215     4.03662
                   |
             _cons |  -2.100061   .4325215    -4.86   0.000    -2.947787   -1.252334
------------------------------------------------------------------------------------

.                 eststo m5a_i

.                 margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .047619   .0268303     1.77   0.076    -.0049674    .1002055
          2  |   .0344828    .033883     1.02   0.309    -.0319267    .1008922
          3  |   .0208333   .0145771     1.43   0.153    -.0077373     .049404
          4  |    .037037   .0148377     2.50   0.013     .0079558    .0661183
          5  |   .0409836   .0179489     2.28   0.022     .0058044    .0761628
          6  |   .0553191   .0149124     3.71   0.000     .0260915    .0845468
          7  |   .0159574   .0091392     1.75   0.081    -.0019551      .03387
          8  |   .0416667   .0128987     3.23   0.001     .0163856    .0669477
          9  |   .0526316   .0170759     3.08   0.002     .0191634    .0860998
         10  |   .1272727   .0449392     2.83   0.005     .0391935     .215352
         11  |   .2131148   .0524321     4.06   0.000     .1103497    .3158799
------------------------------------------------------------------------------

.                 eststo share_vet_ps_a_mf_i

.                 estimates restore m5a_i 
(results m5a_i are active now)

.                 margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1090909   .0420368     2.60   0.009     .0267003    .1914815
          2  |   .0322581   .0317335     1.02   0.309    -.0299385    .0944546
          3  |    .020202     .01414     1.43   0.153    -.0075118    .0479158
          4  |   .0367647   .0161366     2.28   0.023     .0051375    .0683919
          5  |   .0296296   .0145937     2.03   0.042     .0010265    .0582327
          6  |   .0171674   .0085097     2.02   0.044     .0004887    .0338461
          7  |   .0164835    .009438     1.75   0.081    -.0020146    .0349817
          8  |   .0213904   .0105802     2.02   0.043     .0006536    .0421271
          9  |   .0366492   .0135959     2.70   0.007     .0100017    .0632967
         10  |   .0266667   .0186031     1.43   0.152    -.0097947     .063128
         11  |   .0701754   .0338342     2.07   0.038     .0038617    .1364892
------------------------------------------------------------------------------

.                 eststo share_vet_ps_a_ll_i

.                 coefplot        (share_vet_ps_a_mf_i, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}"
> )) /// ) ///
>                                         (share_vet_ps_a_ll_i, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}"))
>  ///)
>                                         , vert ytitle("Probability of" "Sharing the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                         ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) omitted ///
>                                         coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                         name(share_vet_ps_A_i, replace) fxsize(100)

.                 logit share_vet_both_yes i.vet_MetteF##i.PS_V 

note: 0.vet_MetteF#4.PS_V != 0 predicts failure perfectly
      0.vet_MetteF#4.PS_V dropped and 128 obs not used

note: 1.vet_MetteF#4.PS_V omitted because of collinearity
Iteration 0:   log likelihood = -471.21973  
Iteration 1:   log likelihood = -456.73591  
Iteration 2:   log likelihood = -452.96161  
Iteration 3:   log likelihood = -452.91276  
Iteration 4:   log likelihood = -452.91256  
Iteration 5:   log likelihood = -452.91256  

Logistic regression                             Number of obs     =      2,674
                                                LR chi2(20)       =      36.61
                                                Prob > chi2       =     0.0130
Log likelihood = -452.91256                     Pseudo R2         =     0.0389

------------------------------------------------------------------------------------
share_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
      1.vet_MetteF |    .891261   .5569796     1.60   0.110     -.200399    1.982921
                   |
              PS_V |
                1  |  -.0287681   .7385149    -0.04   0.969    -1.476231    1.418695
                2  |  -.7404001    .842676    -0.88   0.380    -2.392015    .9112145
                3  |   .5854852   .6149565     0.95   0.341    -.6198075    1.790778
                4  |  -1.932986   1.055343    -1.83   0.067    -4.001419    .1354476
                5  |   .0753494   .6788369     0.11   0.912    -1.255146    1.405845
                6  |  -.1415636   .8453451    -0.17   0.867    -1.798409    1.515282
                7  |   1.332955   .5704631     2.34   0.019     .2148679    2.451042
                8  |   1.107872   .6187915     1.79   0.073    -.1049374    2.320681
                9  |    .531781   .8510089     0.62   0.532    -1.136166    2.199728
               10  |   1.435085   .7551157     1.90   0.057     -.044915    2.915084
                   |
   vet_MetteF#PS_V |
             0  4  |          0  (empty)
             1  1  |   .0945558   .8768124     0.11   0.914    -1.623965    1.813077
             1  2  |   .5632416   .9729353     0.58   0.563    -1.343677     2.47016
             1  3  |  -.9308606   .7951908    -1.17   0.242    -2.489406    .6276848
             1  4  |          0  (omitted)
             1  5  |   .3994812    .802694     0.50   0.619     -1.17377    1.972733
             1  6  |   .0589313   .9977543     0.06   0.953    -1.896631    2.014494
             1  7  |  -1.389385   .7788211    -1.78   0.074    -2.915846    .1370764
             1  8  |  -1.618697   .9127377    -1.77   0.076     -3.40763    .1702358
             1  9  |  -.2175319   1.048363    -0.21   0.836    -2.272286    1.837222
             1 10  |  -.5017962   .9805774    -0.51   0.609    -2.423693      1.4201
                   |
             _cons |  -3.770459   .4523365    -8.34   0.000    -4.657023   -2.883896
------------------------------------------------------------------------------------

.                 eststo m5b_i

.                 margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post
estimates post: matrix has missing values
r(504);

end of do-file

r(504);

. do "C:\Users\RATP~1.INT\AppData\Local\Temp\18\STDb9cc_000000.tmp"

. ***Commenting***
.         *Vets
.         logit comment_vet_both_yes i.vet_MetteF##c.PS_A

Iteration 0:   log likelihood = -345.55935  
Iteration 1:   log likelihood = -343.71065  
Iteration 2:   log likelihood = -343.66722  
Iteration 3:   log likelihood = -343.66719  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =       3.78
                                                Prob > chi2       =     0.2857
Log likelihood = -343.66719                     Pseudo R2         =     0.0055

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |   -.789591   .6143198    -1.29   0.199    -1.993636    .4144536
                PS_A |  -.0016796   .0673574    -0.02   0.980    -.1336978    .1303385
                     |
   vet_MetteF#c.PS_A |
                  1  |   .1368739   .0979081     1.40   0.162    -.0550224    .3287702
                     |
               _cons |  -3.583354   .4020923    -8.91   0.000    -4.371441   -2.795268
--------------------------------------------------------------------------------------

.         eststo m3a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(vet_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //marginally significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =    2.947367

2._at        : vet_MetteF      =           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0184426    .005045            A
          2  |   .0352861   .0070425            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0168435   .0087377     1.93   0.054    -.0002821    .0339691
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =    2.947367

2._at        : vet_MetteF      =           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0269015   .0061835            A
          2  |   .0266857   .0060816            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0002158   .0086545    -0.02   0.980    -.0171783    .0167467
------------------------------------------------------------------------------

.         estimates restore m3a
(results m3a are active now)

.         margins, at(vet_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_A            =           0

2._at        : vet_MetteF      =           1
               PS_A            =           1

3._at        : vet_MetteF      =           1
               PS_A            =           2

4._at        : vet_MetteF      =           1
               PS_A            =           3

5._at        : vet_MetteF      =           1
               PS_A            =           4

6._at        : vet_MetteF      =           1
               PS_A            =           5

7._at        : vet_MetteF      =           1
               PS_A            =           6

8._at        : vet_MetteF      =           1
               PS_A            =           7

9._at        : vet_MetteF      =           1
               PS_A            =           8

10._at       : vet_MetteF      =           1
               PS_A            =           9

11._at       : vet_MetteF      =           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0124569   .0057135     2.18   0.029     .0012587    .0236551
          2  |   .0142345   .0055959     2.54   0.011     .0032667    .0252023
          3  |   .0162615    .005365     3.03   0.002     .0057464    .0267767
          4  |   .0185718   .0050249     3.70   0.000     .0087233    .0284204
          5  |   .0212033   .0046234     4.59   0.000     .0121417    .0302649
          6  |   .0241984   .0043123     5.61   0.000     .0157465    .0326503
          7  |   .0276046   .0044248     6.24   0.000     .0189321    .0362771
          8  |   .0314749   .0053754     5.86   0.000     .0209393    .0420104
          9  |   .0358677   .0073449     4.88   0.000      .021472    .0502634
         10  |   .0408478   .0103085     3.96   0.000     .0206435    .0610521
         11  |    .046486   .0142559     3.26   0.001     .0185449    .0744271
------------------------------------------------------------------------------

.         eststo comment_vet_ps_a_mf

.         estimates restore m3a 
(results m3a are active now)

.         margins, at(vet_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_A            =           0

2._at        : vet_MetteF      =           0
               PS_A            =           1

3._at        : vet_MetteF      =           0
               PS_A            =           2

4._at        : vet_MetteF      =           0
               PS_A            =           3

5._at        : vet_MetteF      =           0
               PS_A            =           4

6._at        : vet_MetteF      =           0
               PS_A            =           5

7._at        : vet_MetteF      =           0
               PS_A            =           6

8._at        : vet_MetteF      =           0
               PS_A            =           7

9._at        : vet_MetteF      =           0
               PS_A            =           8

10._at       : vet_MetteF      =           0
               PS_A            =           9

11._at       : vet_MetteF      =           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0270314   .0105753     2.56   0.011     .0063042    .0477585
          2  |   .0269872   .0089788     3.01   0.003     .0093891    .0445853
          3  |   .0269431   .0074723     3.61   0.000     .0122978    .0415885
          4  |   .0268991   .0061176     4.40   0.000     .0149089    .0388893
          5  |   .0268552   .0050327     5.34   0.000     .0169913    .0367191
          6  |   .0268113   .0044142     6.07   0.000     .0181596    .0354631
          7  |   .0267676    .004454     6.01   0.000     .0180379    .0354972
          8  |   .0267238    .005131     5.21   0.000     .0166674    .0367803
          9  |   .0266802   .0062364     4.28   0.000      .014457    .0389034
         10  |   .0266366   .0075815     3.51   0.000     .0117771    .0414961
         11  |   .0265931   .0090569     2.94   0.003     .0088419    .0443443
------------------------------------------------------------------------------

.         eststo comment_vet_ps_a_ll

.         coefplot        (comment_vet_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// )
>  ///
>                                 (comment_vet_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_vet_ps_A, replace) fxsize(100)

.         logit comment_vet_both_yes i.vet_MetteF##c.PS_V

Iteration 0:   log likelihood = -345.53222  
Iteration 1:   log likelihood = -344.92809  
Iteration 2:   log likelihood =  -344.9255  
Iteration 3:   log likelihood =  -344.9255  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =       1.21
                                                Prob > chi2       =     0.7498
Log likelihood =  -344.9255                     Pseudo R2         =     0.0018

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        1.vet_MetteF |  -.0359866    .414781    -0.09   0.931    -.8489424    .7769692
                PS_V |   .0416462   .0575381     0.72   0.469    -.0711263    .1544188
                     |
   vet_MetteF#c.PS_V |
                  1  |   .0069029   .0817086     0.08   0.933    -.1532431    .1670489
                     |
               _cons |  -3.756745   .2900504   -12.95   0.000    -4.325233   -3.188256
--------------------------------------------------------------------------------------

.         eststo m3b

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(vet_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =    1.023965

2._at        : vet_MetteF      =           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0231346   .0056332            A
          2  |   .0302046   .0063275            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |     .00707   .0084072     0.84   0.400    -.0094078    .0235478
------------------------------------------------------------------------------

.         margins, at(vet_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =    1.023965

2._at        : vet_MetteF      =           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0237972   .0056728            A
          2  |   .0299122   .0064333            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |    .006115    .008443     0.72   0.469    -.0104331     .022663
------------------------------------------------------------------------------

.         estimates restore m3b
(results m3b are active now)

.         margins, at(vet_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           1
               PS_V            =           0

2._at        : vet_MetteF      =           1
               PS_V            =           1

3._at        : vet_MetteF      =           1
               PS_V            =           2

4._at        : vet_MetteF      =           1
               PS_V            =           3

5._at        : vet_MetteF      =           1
               PS_V            =           4

6._at        : vet_MetteF      =           1
               PS_V            =           5

7._at        : vet_MetteF      =           1
               PS_V            =           6

8._at        : vet_MetteF      =           1
               PS_V            =           7

9._at        : vet_MetteF      =           1
               PS_V            =           8

10._at       : vet_MetteF      =           1
               PS_V            =           9

11._at       : vet_MetteF      =           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0220374   .0063902     3.45   0.001     .0095129    .0345619
          2  |   .0231083   .0056506     4.09   0.000     .0120334    .0341833
          3  |   .0242301   .0049666     4.88   0.000     .0144958    .0339643
          4  |   .0254048    .004452     5.71   0.000     .0166791    .0341305
          5  |    .026635    .004281     6.22   0.000     .0182444    .0350255
          6  |    .027923    .004617     6.05   0.000     .0188739     .036972
          7  |   .0292714     .00549     5.33   0.000     .0185112    .0400316
          8  |   .0306829    .006812     4.50   0.000     .0173316    .0440341
          9  |   .0321602   .0084854     3.79   0.000      .015529    .0487913
         10  |   .0337061   .0104484     3.23   0.001     .0132276    .0541846
         11  |   .0353236   .0126694     2.79   0.005     .0104921    .0601552
------------------------------------------------------------------------------

.         eststo comment_vet_ps_v_mf

.         estimates restore m3b           
(results m3b are active now)

.         margins, at(vet_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : vet_MetteF      =           0
               PS_V            =           0

2._at        : vet_MetteF      =           0
               PS_V            =           1

3._at        : vet_MetteF      =           0
               PS_V            =           2

4._at        : vet_MetteF      =           0
               PS_V            =           3

5._at        : vet_MetteF      =           0
               PS_V            =           4

6._at        : vet_MetteF      =           0
               PS_V            =           5

7._at        : vet_MetteF      =           0
               PS_V            =           6

8._at        : vet_MetteF      =           0
               PS_V            =           7

9._at        : vet_MetteF      =           0
               PS_V            =           8

10._at       : vet_MetteF      =           0
               PS_V            =           9

11._at       : vet_MetteF      =           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0228264   .0064697     3.53   0.000     .0101461    .0355068
          2  |   .0237741   .0056908     4.18   0.000     .0126202    .0349279
          3  |     .02476    .004992     4.96   0.000     .0149759    .0345442
          4  |   .0257858   .0044921     5.74   0.000     .0169815    .0345902
          5  |   .0268529   .0043597     6.16   0.000      .018308    .0353979
          6  |   .0279629   .0047325     5.91   0.000     .0186875    .0372384
          7  |   .0291175   .0056124     5.19   0.000     .0181174    .0401176
          8  |   .0303182   .0069032     4.39   0.000     .0167881    .0438482
          9  |   .0315668   .0085093     3.71   0.000     .0148888    .0482447
         10  |    .032865   .0103701     3.17   0.002       .01254      .05319
         11  |   .0342148   .0124533     2.75   0.006     .0098068    .0586229
------------------------------------------------------------------------------

.         eststo comment_vet_ps_v_ll

.         coefplot        (comment_vet_ps_v_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.05) label("Post from {it:Social Democrats}")) /// )
>  ///
>                                 (comment_vet_ps_v_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.05) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Veterans", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12) size(medium)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_vet_ps_V, replace) fxsize(90)

.         *Immigrants
.         logit comment_immi_both_yes i.immigrants_MetteF##c.PS_A

Iteration 0:   log likelihood =  -418.2503  
Iteration 1:   log likelihood = -414.41636  
Iteration 2:   log likelihood = -414.28534  
Iteration 3:   log likelihood = -414.28521  
Iteration 4:   log likelihood = -414.28521  

Logistic regression                             Number of obs     =      2,803
                                                LR chi2(3)        =       7.93
                                                Prob > chi2       =     0.0475
Log likelihood = -414.28521                     Pseudo R2         =     0.0095

------------------------------------------------------------------------------------------
   comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |  -1.242555   .5614201    -2.21   0.027    -2.342918    -.142192
                    PS_A |  -.0158326   .0533547    -0.30   0.767     -.120406    .0887407
                         |
immigrants_MetteF#c.PS_A |
                      1  |   .1375529   .0896381     1.53   0.125    -.0381346    .3132403
                         |
                   _cons |  -3.048176   .3162175    -9.64   0.000     -3.66795   -2.428401
------------------------------------------------------------------------------------------

.         eststo m4a

.         *Comparing respondents -1sd and +1sd
.         local PS_A_minus_sd=PS_A_minus_sd

.         local PS_A_plus_sd=PS_A_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           1
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0192279   .0052151            A
          2  |   .0344819   .0071306            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0152539   .0089616     1.70   0.089    -.0023105    .0328183
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_A=(`PS_A_minus_sd' `PS_A_plus_sd')) pwcompare(groups effects) //not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =    2.947367

2._at        : immigrants_MetteF=           0
               PS_A            =    7.874609

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0433206   .0076971            A
          2  |   .0402002   .0072643            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0031203   .0105211    -0.30   0.767    -.0237412    .0175005
------------------------------------------------------------------------------

.         estimates restore m4a
(results m4a are active now)

.         margins, at(immigrants_MetteF=(1) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_A            =           0

2._at        : immigrants_MetteF=           1
               PS_A            =           1

3._at        : immigrants_MetteF=           1
               PS_A            =           2

4._at        : immigrants_MetteF=           1
               PS_A            =           3

5._at        : immigrants_MetteF=           1
               PS_A            =           4

6._at        : immigrants_MetteF=           1
               PS_A            =           5

7._at        : immigrants_MetteF=           1
               PS_A            =           6

8._at        : immigrants_MetteF=           1
               PS_A            =           7

9._at        : immigrants_MetteF=           1
               PS_A            =           8

10._at       : immigrants_MetteF=           1
               PS_A            =           9

11._at       : immigrants_MetteF=           1
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0135099   .0061825     2.19   0.029     .0013924    .0256274
          2  |    .015232   .0059629     2.55   0.011     .0035449     .026919
          3  |   .0171697   .0056281     3.05   0.002     .0061388    .0282006
          4  |   .0193491   .0051901     3.73   0.000     .0091766    .0295216
          5  |    .021799   .0047095     4.63   0.000     .0125685    .0310295
          6  |   .0245514    .004358     5.63   0.000     .0160099    .0330929
          7  |   .0276414   .0044835     6.17   0.000     .0188539    .0364289
          8  |    .031108   .0054682     5.69   0.000     .0203904    .0418255
          9  |   .0349936   .0074277     4.71   0.000     .0204355    .0495517
         10  |   .0393449   .0102974     3.82   0.000     .0191625    .0595274
         11  |   .0442125   .0140434     3.15   0.002     .0166879    .0717371
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_mf

.         estimates restore m4a 
(results m4a are active now)

.         margins, at(immigrants_MetteF=(0) PS_A=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,803
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_A            =           0

2._at        : immigrants_MetteF=           0
               PS_A            =           1

3._at        : immigrants_MetteF=           0
               PS_A            =           2

4._at        : immigrants_MetteF=           0
               PS_A            =           3

5._at        : immigrants_MetteF=           0
               PS_A            =           4

6._at        : immigrants_MetteF=           0
               PS_A            =           5

7._at        : immigrants_MetteF=           0
               PS_A            =           6

8._at        : immigrants_MetteF=           0
               PS_A            =           7

9._at        : immigrants_MetteF=           0
               PS_A            =           8

10._at       : immigrants_MetteF=           0
               PS_A            =           9

11._at       : immigrants_MetteF=           0
               PS_A            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0452963   .0136747     3.31   0.001     .0184944    .0720982
          2  |   .0446165   .0114557     3.89   0.000     .0221638    .0670693
          3  |   .0439465   .0094092     4.67   0.000     .0255047    .0623883
          4  |   .0432861   .0076105     5.69   0.000     .0283698    .0582024
          5  |   .0426351   .0062018     6.87   0.000     .0304797    .0547905
          6  |   .0419935   .0054113     7.76   0.000     .0313875    .0525996
          7  |   .0413612   .0054356     7.61   0.000     .0307076    .0520148
          8  |    .040738   .0062019     6.57   0.000     .0285825    .0528934
          9  |   .0401237   .0074357     5.40   0.000     .0255501    .0546973
         10  |   .0395184    .008906     4.44   0.000     .0220628    .0569739
         11  |   .0389218   .0104825     3.71   0.000     .0183764    .0594671
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_ll

.         coefplot        (comment_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// 
> ) ///
>                                 (comment_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("Probability of" "Commenting the post", size(medium)) xtitle("Sympathy for {it:Social Democrats}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_immi_ps_A, replace)        fxsize(100)             

.         logit comment_immi_both_yes i.immigrants_MetteF##c.PS_V

Iteration 0:   log likelihood = -418.21544  
Iteration 1:   log likelihood = -414.59599  
Iteration 2:   log likelihood =  -414.4831  
Iteration 3:   log likelihood = -414.48301  
Iteration 4:   log likelihood = -414.48301  

Logistic regression                             Number of obs     =      2,802
                                                LR chi2(3)        =       7.46
                                                Prob > chi2       =     0.0585
Log likelihood = -414.48301                     Pseudo R2         =     0.0089

------------------------------------------------------------------------------------------
   comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
     1.immigrants_MetteF |  -.8802613    .363598    -2.42   0.015      -1.5929   -.1676223
                    PS_V |   -.066111   .0484974    -1.36   0.173    -.1611642    .0289422
                         |
immigrants_MetteF#c.PS_V |
                      1  |   .1084083   .0763232     1.42   0.155    -.0411824     .257999
                         |
                   _cons |  -2.890804    .211011   -13.70   0.000    -3.304378    -2.47723
------------------------------------------------------------------------------------------

.         eststo m4b      

.         *Comparing respondents -1sd and +1sd
.         local PS_V_minus_sd=PS_V_minus_sd

.         local PS_V_plus_sd=PS_V_plus_sd

.         margins, at(immigrants_MetteF=(1) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           1
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0234821   .0056995            A
          2  |   .0296239   .0064094            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |   .0061418   .0085416     0.72   0.472    -.0105993     .022883
------------------------------------------------------------------------------

.         margins, at(immigrants_MetteF=(0) PS_V=(`PS_V_minus_sd' `PS_V_plus_sd')) pwcompare(groups effects) // not significant

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =    1.023965

2._at        : immigrants_MetteF=           0
               PS_V            =    6.666256

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0493363   .0082218            A
          2  |   .0345056   .0069221            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0148307   .0108185    -1.37   0.170    -.0360345    .0063732
------------------------------------------------------------------------------

.         estimates restore m4b
(results m4b are active now)

.         margins, at(immigrants_MetteF=(1) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           1
               PS_V            =           0

2._at        : immigrants_MetteF=           1
               PS_V            =           1

3._at        : immigrants_MetteF=           1
               PS_V            =           2

4._at        : immigrants_MetteF=           1
               PS_V            =           3

5._at        : immigrants_MetteF=           1
               PS_V            =           4

6._at        : immigrants_MetteF=           1
               PS_V            =           5

7._at        : immigrants_MetteF=           1
               PS_V            =           6

8._at        : immigrants_MetteF=           1
               PS_V            =           7

9._at        : immigrants_MetteF=           1
               PS_V            =           8

10._at       : immigrants_MetteF=           1
               PS_V            =           9

11._at       : immigrants_MetteF=           1
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0225092    .006515     3.45   0.001     .0097399    .0352784
          2  |   .0234589   .0057181     4.10   0.000     .0122516    .0346661
          3  |   .0244476   .0049956     4.89   0.000     .0146564    .0342389
          4  |    .025477   .0044684     5.70   0.000      .016719    .0342349
          5  |   .0265485   .0043133     6.16   0.000     .0180945    .0350024
          6  |   .0276637   .0046796     5.91   0.000      .018492    .0368355
          7  |   .0288245   .0055723     5.17   0.000      .017903     .039746
          8  |   .0300324    .006889     4.36   0.000     .0165302    .0435347
          9  |   .0312894   .0085288     3.67   0.000     .0145732    .0480056
         10  |   .0325971    .010429     3.13   0.002     .0121567    .0530376
         11  |   .0339577   .0125567     2.70   0.007     .0093469    .0585684
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_mf

.         estimates restore m4b 
(results m4b are active now)

.         margins, at(immigrants_MetteF=(0) PS_V=(0(1)10)) post

Adjusted predictions                            Number of obs     =      2,802
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : immigrants_MetteF=           0
               PS_V            =           0

2._at        : immigrants_MetteF=           0
               PS_V            =           1

3._at        : immigrants_MetteF=           0
               PS_V            =           2

4._at        : immigrants_MetteF=           0
               PS_V            =           3

5._at        : immigrants_MetteF=           0
               PS_V            =           4

6._at        : immigrants_MetteF=           0
               PS_V            =           5

7._at        : immigrants_MetteF=           0
               PS_V            =           6

8._at        : immigrants_MetteF=           0
               PS_V            =           7

9._at        : immigrants_MetteF=           0
               PS_V            =           8

10._at       : immigrants_MetteF=           0
               PS_V            =           9

11._at       : immigrants_MetteF=           0
               PS_V            =          10

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     .05261   .0105173     5.00   0.000     .0319966    .0732235
          2  |   .0494107   .0082692     5.98   0.000     .0332034     .065618
          3  |   .0463964   .0065876     7.04   0.000     .0334849    .0593079
          4  |   .0435576   .0055966     7.78   0.000     .0325883    .0545268
          5  |    .040885   .0053522     7.64   0.000     .0303949     .051375
          6  |   .0383698    .005706     6.72   0.000     .0271862    .0495533
          7  |   .0360035   .0063907     5.63   0.000     .0234779    .0485291
          8  |   .0337781   .0071949     4.69   0.000     .0196764    .0478798
          9  |   .0316857   .0080006     3.96   0.000     .0160048    .0473665
         10  |   .0297189    .008751     3.40   0.001     .0125673    .0468704
         11  |   .0278707   .0094209     2.96   0.003     .0094061    .0463353
------------------------------------------------------------------------------

.         eststo comment_immi_ps_a_ll

.         coefplot        (comment_immi_ps_a_mf, msymbol(circle) msize(medsmall) mcolor(gs8)   mlcolor(black) ciopts(recast(rarea) color(gs8%40) ) offset(-.02) label("Post from {it:Social Democrats}")) /// 
> ) ///
>                                 (comment_immi_ps_a_ll, msymbol(circle) msize(medsmall) mcolor(gs14)  mlcolor(black) ciopts(recast(rarea) color(gs10%40)) offset(.02) label("Post from {it:Venstre}")) ///)
>                                 , vert ytitle("", size(medium)) xtitle("Sympathy for {it:Venstre}") title("Immigrants", size(large)) ///
>                                 ylabel(0(.02).1,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at= "0" 2._at= "1" 3._at= "2" 4._at= "3" 5._at= "4" 6._at= "5" 7._at= "6" 8._at= "7" 9._at= "8"     10._at= "9"     11._at= "10" ) ///
>                                 name(comment_immi_ps_V, replace)        fxsize(90)              

.         graph close

.         *FIGURE 2: Commenting as a Function of Party Sympathy
.         grc1leg2  comment_vet_ps_A comment_vet_ps_V comment_immi_ps_A comment_immi_ps_V , xsize(6) ycommon pos(12) title("", size(medlarge)) note("Note: Estimates with 95% confidence intervals (n=2,802/2,
> 803)")

.         graph export FIGURE_2.png, replace      
(file FIGURE_2.png written in PNG format)

. 
end of do-file

. do "C:\Users\RATP~1.INT\AppData\Local\Temp\18\STDb9cc_000000.tmp"

. **Comments //some effects       
.         *like
.         logit like_vet_both_yes i.comments_vet //lells likely to like when exposed to negative comments

Iteration 0:   log likelihood = -1502.4867  
Iteration 1:   log likelihood = -1497.9807  
Iteration 2:   log likelihood = -1497.9692  
Iteration 3:   log likelihood = -1497.9692  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       9.04
                                                Prob > chi2       =     0.0109
Log likelihood = -1497.9692                     Pseudo R2         =     0.0030

-----------------------------------------------------------------------------------
like_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
     comments_vet |
               1  |  -.1617297   .1091828    -1.48   0.139     -.375724    .0522646
               2  |  -.3366269   .1124788    -2.99   0.003    -.5570812   -.1161726
                  |
            _cons |  -1.234666   .0747559   -16.52   0.000    -1.381185   -1.088147
-----------------------------------------------------------------------------------

.         eststo m41

.         margins, at(comments_vet=(0 1 2)) pwcompare(groups effects)

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .2253659   .0130506            B
          2  |   .1983887   .0126551           AB
          3  |   .1720322   .0119707           A 
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0269771   .0181788    -1.48   0.138     -.062607    .0086527
     3 vs 1  |  -.0533337   .0177092    -3.01   0.003     -.088043   -.0186243
     3 vs 2  |  -.0263565   .0174198    -1.51   0.130    -.0604987    .0077856
------------------------------------------------------------------------------

.         margins, at(comments_vet=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(like_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2253659   .0130506    17.27   0.000     .1997871    .2509446
          2  |   .1983887   .0126551    15.68   0.000     .1735852    .2231923
          3  |   .1720322   .0119707    14.37   0.000     .1485701    .1954943
------------------------------------------------------------------------------

.         eststo like_vet_comment

.         coefplot        (like_vet_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Probability of" "Liking the post", size(medlarge)) xtitle("") title("Liking post on veterans", size(large)) ///
>                                 ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(like_vet_comment, replace)

.         logit like_immi_both_yes i.comments_immi //model insignificant

Iteration 0:   log likelihood = -1106.2933  
Iteration 1:   log likelihood =  -1106.128  
Iteration 2:   log likelihood =  -1106.128  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       0.33
                                                Prob > chi2       =     0.8476
Log likelihood =  -1106.128                     Pseudo R2         =     0.0001

------------------------------------------------------------------------------------
like_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
     comments_immi |
                1  |   -.037929   .1353066    -0.28   0.779    -.3031251     .227267
                2  |  -.0792308   .1378441    -0.57   0.565    -.3494002    .1909386
                   |
             _cons |  -1.952637   .0948226   -20.59   0.000    -2.138485   -1.766788
------------------------------------------------------------------------------------

.         eststo m42

.         margins, at(comments_immi=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(like_immi_both_yes), predict()

1._at        : comments_immi   =           0

2._at        : comments_immi   =           1

3._at        : comments_immi   =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1242662    .010319    12.04   0.000     .1040413     .144491
          2  |    .120197   .0102072    11.78   0.000     .1001913    .1402028
          3  |   .1158975   .0102515    11.31   0.000     .0958049      .13599
------------------------------------------------------------------------------

.         eststo like_immi_comment

.         coefplot        (like_immi_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Probability of" "Liking the post", size(medlarge)) xtitle("") title("Liking post on immigrants", size(medium)) ///
>                                 ylabel(0(.05).25,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(like_immi_comment, replace)        

.         graph combine like_vet_comment like_immi_comment, col(1) ycommon title("") name(liking_comment, replace)

.         
.         *comment
.         logit comment_vet_both_yes i.comments_vet //Less likely to comment when exposed to negative comments (marginally significant)

Iteration 0:   log likelihood =  -354.6846  
Iteration 1:   log likelihood = -353.07795  
Iteration 2:   log likelihood = -353.05496  
Iteration 3:   log likelihood = -353.05496  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       3.26
                                                Prob > chi2       =     0.1960
Log likelihood = -353.05496                     Pseudo R2         =     0.0046

--------------------------------------------------------------------------------------
comment_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
        comments_vet |
                  1  |  -.0383783    .266625    -0.14   0.886    -.5609538    .4841971
                  2  |  -.4915456   .3015269    -1.63   0.103    -1.082528    .0994364
                     |
               _cons |  -3.501545   .1853061   -18.90   0.000    -3.864739   -3.138352
--------------------------------------------------------------------------------------

.         eststo m43

.         margins, at(comments_vet=(0 1 2)) pwcompare(groups effects)

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0292683   .0052649            A
          2  |   .0281974   .0052531            A
          3  |   .0181087   .0042294            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0010709   .0074373    -0.14   0.886    -.0156478     .013506
     3 vs 1  |  -.0111596   .0067533    -1.65   0.098    -.0243958    .0020765
     3 vs 2  |  -.0100887   .0067441    -1.50   0.135     -.023307    .0031296
------------------------------------------------------------------------------

.         margins, at(comments_vet=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(comment_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0292683   .0052649     5.56   0.000     .0189494    .0395872
          2  |   .0281974   .0052531     5.37   0.000     .0179014    .0384933
          3  |   .0181087   .0042294     4.28   0.000     .0098191    .0263982
------------------------------------------------------------------------------

.         eststo comment_vet_comment

.         coefplot        (comment_vet_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Probability of" "Commenting the post", size(medlarge)) xtitle("") title("Commenting post on veterans", size(large)) ///
>                                 ylabel(0(.01).05,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(comment_vet_comment, replace)

.         logit comment_immi_both_yes i.comments_immi //model insignificant

Iteration 0:   log likelihood = -428.67895  
Iteration 1:   log likelihood = -428.60895  
Iteration 2:   log likelihood = -428.60892  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       0.14
                                                Prob > chi2       =     0.9324
Log likelihood = -428.60892                     Pseudo R2         =     0.0002

---------------------------------------------------------------------------------------
comment_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
        comments_immi |
                   1  |  -.0883158    .252385    -0.35   0.726    -.5829813    .4063498
                   2  |  -.0140083   .2504683    -0.06   0.955    -.5049171    .4769005
                      |
                _cons |  -3.369322   .1744245   -19.32   0.000    -3.711188   -3.027456
---------------------------------------------------------------------------------------

.         eststo m44

.         margins, at(comments_immi=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(comment_immi_both_yes), predict()

1._at        : comments_immi   =           0

2._at        : comments_immi   =           1

3._at        : comments_immi   =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0332681   .0056097     5.93   0.000     .0222732     .044263
          2  |   .0305419   .0054011     5.65   0.000      .019956    .0411278
          3  |   .0328205   .0057059     5.75   0.000     .0216372    .0440039
------------------------------------------------------------------------------

.         eststo comment_immi_comment

.         coefplot        (comment_immi_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Probability of" "Commenting the post", size(medlarge)) xtitle("") title("Commenting post on immigrants", size(large)) ///
>                                 ylabel(0(.01).05,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(comment_immi_comment, replace)     

.         graph combine comment_vet_comment comment_immi_comment, col(1) ycommon title("") name(commenting_comment, replace)

.         
.         *share
.         logit share_vet_both_yes i.comments_vet //Less likely to comment when exposed to negative comments (marginally significant)

Iteration 0:   log likelihood = -501.13475  
Iteration 1:   log likelihood = -499.25737  
Iteration 2:   log likelihood = -499.23776  
Iteration 3:   log likelihood = -499.23776  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       3.79
                                                Prob > chi2       =     0.1500
Log likelihood = -499.23776                     Pseudo R2         =     0.0038

------------------------------------------------------------------------------------
share_vet_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
      comments_vet |
                1  |  -.2814256   .2215822    -1.27   0.204    -.7157188    .1528677
                2  |  -.4328641   .2309007    -1.87   0.061    -.8854212     .019693
                   |
             _cons |  -2.970414   .1450022   -20.49   0.000    -3.254614   -2.686215
------------------------------------------------------------------------------------

.         eststo m45

.         margins, at(comments_vet=(0 1 2)) pwcompare(groups effects)

Pairwise comparisons of adjusted predictions
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

-------------------------------------------------
             |            Delta-method Unadjusted
             |     Margin   Std. Err.      Groups
-------------+-----------------------------------
         _at |
          1  |   .0487805   .0067282            A
          2  |   .0372608   .0060104            A
          3  |   .0321932   .0055986            A
-------------------------------------------------
Note: Margins sharing a letter in the group label
      are not significantly different at the 5%
      level.

------------------------------------------------------------------------------
             |            Delta-method    Unadjusted           Unadjusted
             |   Contrast   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
     2 vs 1  |  -.0115197   .0090219    -1.28   0.202    -.0292022    .0061629
     3 vs 1  |  -.0165873   .0087529    -1.90   0.058    -.0337428    .0005681
     3 vs 2  |  -.0050677    .008214    -0.62   0.537    -.0211669    .0110315
------------------------------------------------------------------------------

.         margins, at(comments_vet=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(share_vet_both_yes), predict()

1._at        : comments_vet    =           0

2._at        : comments_vet    =           1

3._at        : comments_vet    =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0487805   .0067282     7.25   0.000     .0355934    .0619676
          2  |   .0372608   .0060104     6.20   0.000     .0254806    .0490411
          3  |   .0321932   .0055986     5.75   0.000       .02122    .0431663
------------------------------------------------------------------------------

.         eststo share_vet_comment

.         coefplot        (share_vet_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Likelihood of" "Sharing the post", size(medlarge)) xtitle("") title("Sharing post on veterans", size(large)) ///
>                                 ylabel(0(.01).05,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(share_vet_comment, replace)

.         logit share_immi_both_yes i.comments_immi //model insignificant

Iteration 0:   log likelihood = -376.37204  
Iteration 1:   log likelihood = -375.27193  
Iteration 2:   log likelihood = -375.26169  
Iteration 3:   log likelihood = -375.26169  

Logistic regression                             Number of obs     =      3,012
                                                LR chi2(2)        =       2.22
                                                Prob > chi2       =     0.3294
Log likelihood = -375.26169                     Pseudo R2         =     0.0030

-------------------------------------------------------------------------------------
share_immi_both_yes |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      comments_immi |
                 1  |    .323663   .2672717     1.21   0.226    -.2001798    .8475059
                 2  |  -.0371959    .292458    -0.13   0.899     -.610403    .5360113
                    |
              _cons |  -3.685875    .202492   -18.20   0.000    -4.082752   -3.288998
-------------------------------------------------------------------------------------

.         eststo m46

.         margins, at(comments_immi=(0 1 2)) post

Adjusted predictions                            Number of obs     =      3,012
Model VCE    : OIM

Expression   : Pr(share_immi_both_yes), predict()

1._at        : comments_immi   =           0

2._at        : comments_immi   =           1

3._at        : comments_immi   =           2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0244618   .0048322     5.06   0.000      .014991    .0339327
          2  |   .0334975   .0056477     5.93   0.000     .0224282    .0445669
          3  |   .0235897   .0048604     4.85   0.000     .0140635     .033116
------------------------------------------------------------------------------

.         eststo share_immi_comment

.         coefplot        (share_immi_comment, msymbol(circle) msize(medlarge) mcolor(gs16)   mlcolor(black) ciopts(recast(rspike) color(black) ) nokey) ///
>                                 , vert ytitle("Likelihood of" "Sharing the post", size(medlarge)) xtitle("") title("Sharing post on immigrants", size(large)) ///
>                                 ylabel(0(.01).05,labsize(medium) gmin gmax) legend(row(1) pos(12)) ///
>                                 coeflabels      (1._at=`" "No" "Comments" "' 2._at=`" "Positive" "Comments" "' 3._at=`" "Negative" "Comments" "', labsize(medium)) ///
>                                 name(share_immi_comment, replace)       

.         graph combine share_vet_comment share_immi_comment, col(1) ycommon title("") name(sharing_comment, replace)

.         graph close

. 
.         
.         *FIGURE 5: Comments have Small Effects on Liking, Commenting and Sharing
.         graph combine liking_comment commenting_comment sharing_comment,  cols(3) graphregion(margin(0 0 0 0)) title("") note("Note: Estimates with 95% confidence intervals (n=3,012)")

.         graph export Figure_5.png, replace
(file Figure_5.png written in PNG format)

. 
end of do-file

